Monthly Archives: October 2024

Life sciences and healthcare organizations across India are using generative AI to build applications that can deliver life-saving impacts — within the country and across the globe.

Among such leading organizations are research centers at the Indian Institute of Technology Madras (IIT Madras) and the Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), intelligent life sciences company Innoplexus and AI-led medical diagnostics platform provider 5C Network.

Central to their work are NVIDIA NIM microservices, including the new Nemotron-4-Mini-Hindi 4B microservice for building sovereign AI applications and large language models (LLMs) in the Hindi language.

The Nemotron-4 Hindi model delivers the highest accuracy across benchmarks in 2 billion to 8 billion model-size categories for Hindi.

With the Indian healthcare market projected to grow from about $180 billion last year to $320 billion by 2028, the new AI model has the potential to dramatically improve healthcare accessibility and efficiency.

To gear up for such growing demands — and to help more patients faster — the Indian government is significantly investing in building foundational AI models designed and developed within the country, including for healthcare, through initiatives like the IndiaAI Mission.

Members of the Indian healthcare ecosystem are leading the charge by advancing neuroscience research, combating antibiotic resistance, accelerating drug discovery, automating diagnostic scan analysis and more — all with AI’s help.

IIT Madras Advances Neuroscience Research With AI 

The IIT Madras Brain Centre is advancing neuroscience research by imaging whole human brains at a cellular level across various ages and brain diseases, and using AI to analyze these vast petabyte-sized primary datasets. The work is opening new avenues for understanding brain structure and function, as well as how they change in disease conditions, accelerating research that could lead to life-saving discoveries.

To make information about the brain more accessible to STEM students and researchers, the center is developing an AI chatbot — using the Nemotron-4 Hindi NIM microservice — that can answer neuroscience-related questions in Hindi.

This builds upon the center’s existing NVIDIA AI-powered knowledge-exploration framework, called Neuro Voyager. Developed using visual question-answering models and LLMs, Neuro Voyager lets researchers submit queries related to brain images and provides highly accurate answers using multimodal information retrieval.

IIT Madras developed Neuro Voyager using both real-world data from research publications and synthetic data.

Using NVIDIA NeMo Retriever, a collection of NIM microservices for information retrieval, the team achieved a 30% increase in accuracy through fine-tuning of the embedding model and further refinement of the framework.

For the tool’s answer-generation portion, the researchers tapped the Llama 3.1 70B NVIDIA NIM microservice, running on NVIDIA DGX systems, which accelerated LLM inference 4x compared with the native model.

IIIT-Delhi-Led Consortium Fights Antimicrobial Resistance Using Generative AI, NVIDIA DGX

A research group at IIIT-Delhi is using the Nemotron-4 Hindi model to collect antibiotic prescription patterns in local languages, including Hindi.

Antimicrobial resistance — among the world’s greatest threats to global health — occurs when bacteria, viruses, fungi and parasites change over time, no longer responding to treatment and increasing the risk of disease spread, severe illness and death.

IIIT-Delhi researchers predict that AI-guided antimicrobial stewardship will be a key component of preventing the tens of millions of deaths that could be caused by antimicrobial resistance between 2025 and 2050.

The researchers’ AI-powered data integration and predictive analytics tool, AMRSense, improves accuracy and speeds time to insights on antimicrobial resistance. Powered by NVIDIA NeMo platform-based natural language processing, AMRSense is designed to be used in hospital and community settings.

This collaborative solution between IIIT-Delhi and a consortium of other research institutions placed second out of over 300 entries in the Trinity Challenge, a competition that calls for data-driven solutions to help tackle global health threats.

IIIT-Delhi is also using NVIDIA DGX systems to build foundation models that can further hone its workflows.

5C Network Uses NVIDIA NIM, MONAI for AI-Powered Medical Imaging

Bengaluru- and Coimbatore-based 5C Network’s Bionic suite of medical imaging tools, based on computer vision and LLMs, is helping transform radiology reporting by reading, detecting and analyzing medical scans and generating comprehensive medical notes that provide actionable insights to support clinicians in decision-making.

Used across India’s largest hospital groups and several marquee hospitals, Bionic detects pathologies in scans, such as lung lesions in X-rays or brain masses in MRIs. It then provides detailed measurements of abnormalities, such as the size, volume or density of lesions to assess disease severity and treatment planning. Finally, Bionic compiles the data into clear, actionable reports with suggested next steps, such as further testing or specialist referrals.

Bionic was developed using the open-source MONAI framework, the NVIDIA TensorRT ecosystem of application programming interfaces for high-performance deep learning inference, and the NVIDIA NeMo platform for custom generative AI.

Using the Nemotron-4 Hindi NIM microservice, 5C Network is now enhancing its client app, which allows patients to ask questions about radiology reports and receive quick, accurate responses in simplified Hindi.

Innoplexus Analyzes Protein Interactions With NVIDIA NIM

Innoplexus, a member of the NVIDIA Inception program for cutting-edge startups, has built an AI-powered life sciences platform for drug discovery powered by NVIDIA NIM, including the AlphaFold2 NIM microservice.

Protein-protein interaction (PPI) is critical to pathogenic and physiologic mechanisms that trigger the onset and progression of diseases. This means understanding PPI can help facilitate effective diagnostic and therapeutic strategies.

Innoplexus performs large-scale PPI predictions up to 500x faster than traditional methods. The company’s platform can analyze 200 million protein interactions in just seconds, tapping into NVIDIA H100 Tensor Core GPU acceleration.

Using NVIDIA NIM microservices, Innoplexus generates synthetic patient data to boost its AI models and performs virtual screenings of 5.8 million small molecules in less than eight hours — 10x faster than without NIM.

Plus, the microservices help Innoplexus identify the most effective, safest drugs within a given set of therapeutic agents with 90% accuracy.​

Using the new Nemotron-4 Hindi model, Innoplexus is developing a tool that will let users easily access and understand information about Ayurveda, a system of traditional medicine native to India, based on Hindi content from key repositories.

Another Innoplexus LLM application, built with the new Hindi model, explains details about user prescriptions and medical reports — based on photos of them — in easy-to-understand terms.

NVIDIA NIM microservices are available as part of the NVIDIA AI Enterprise software platform. Developers can get started with them for free at ai.nvidia.com.

In addition, global system integrators including Infosys, Tata Consultancy Services (TCS), Tech Mahindra and Wipro are collaborating with NVIDIA to help life sciences and healthcare companies accelerate their generative AI adoption.

Learn more about the latest in generative AI and accelerated computing at the NVIDIA AI Summit in India, and subscribe to NVIDIA healthcare news.

Read More

Namaste, vanakkam, sat sri akaal — these are just three forms of greeting in India, a country with 22 constitutionally recognized languages and over 1,500 more recorded by the country’s census. Around 10% of its residents speak English, the internet’s most common language.

As India, the world’s most populous country, forges ahead with rapid digitalization efforts, its enterprises and local startups are developing multilingual AI models that enable more Indians to interact with technology in their primary language. It’s a case study in sovereign AI — the development of domestic AI infrastructure that is built on local datasets and reflects a region’s specific dialects, cultures and practices.

These projects are building language models for Indic languages and English that can power customer service AI agents for businesses, rapidly translate content to broaden access to information, and enable services to more easily reach a diverse population of over 1.4 billion individuals.

To support initiatives like these, NVIDIA has released a small language model for Hindi, India’s most prevalent language with over half a billion speakers. Now available as an NVIDIA NIM microservice, the model, dubbed Nemotron-4-Mini-Hindi-4B, can be easily deployed on any NVIDIA GPU-accelerated system for optimized performance.

Tech Mahindra, an Indian IT services and consulting company, is the first to use the Nemotron Hindi NIM microservice to develop an AI model called Indus 2.0, which is focused on Hindi and dozens of its dialects. Indus 2.0 harnesses Tech Mahindra’s high-quality fine-tuning data to further boost model accuracy, unlocking opportunities for clients in banking, education, healthcare and other industries to deliver localized services.

Tech Mahindra will showcase Indus 2.0 at the NVIDIA AI Summit, taking place Oct. 23-25 in Mumbai. The company also uses NVIDIA NeMo to develop its sovereign large language model (LLM) platform, TeNo.

NVIDIA NIM Makes AI Adoption for Hindi as Easy as Ek, Do, Teen

The Nemotron Hindi model has 4 billion parameters and is derived from Nemotron-4 15B, a 15-billion parameter multilingual language model developed by NVIDIA. The model was pruned, distilled and trained with a combination of real-world Hindi data, synthetic Hindi data and an equal amount of English data using NVIDIA NeMo, an end-to-end, cloud-native framework and suite of microservices for developing generative AI.

The dataset was created with NVIDIA NeMo Curator, which improves generative AI model accuracy by processing high-quality multimodal data at scale for training and customization. NeMo Curator uses NVIDIA RAPIDS libraries to accelerate data processing pipelines on multi-node GPU systems, lowering processing time and total cost of ownership. It also provides pre-built pipelines and building blocks for synthetic data generation, data filtering, classification and deduplication to process high-quality data.

After fine-tuning with NeMo, the final model leads on multiple accuracy benchmarks for AI models with up to 8 billion parameters. Packaged as a NIM microservice, it can be easily harnessed to support use cases across industries such as education, retail and healthcare.

It’s available as part of the NVIDIA AI Enterprise software platform, which gives businesses access to additional resources, including technical support and enterprise-grade security, to streamline AI development for production environments.

Bevy of Businesses Serves Multilingual Population

Innovators, major enterprises and global systems integrators across India are building customized language models using NVIDIA NeMo.

Companies in the NVIDIA Inception program for cutting-edge startups are using NeMo to develop AI models for several Indic languages.

Sarvam AI offers enterprise customers speech-to-text, text-to-speech, translation and data parsing models. The company developed Sarvam 1, India’s first homegrown, multilingual LLM, which was trained from scratch on domestic AI infrastructure powered by NVIDIA H100 Tensor Core GPUs.

Sarvam 1 — developed using NVIDIA AI Enterprise software including NeMo Curator and NeMo Framework — supports English and 10 major Indian languages, including Bengali, Marathi, Tamil and Telugu.

Sarvam AI also uses NVIDIA NIM microservices, NVIDIA Riva for conversational AI, NVIDIA TensorRT-LLM software and NVIDIA Triton Inference Server to optimize and deploy conversational AI agents with sub-second latency.

Another Inception startup, Gnani.ai, built a multilingual speech-to-speech LLM that powers AI customer service assistants that handle around 10 million real-time voice interactions daily for over 150 banking, insurance and financial services companies across India and the U.S. The model supports 14 languages and was trained on over 14 million hours of conversational speech data using NVIDIA Hopper GPUs and NeMo Framework.

Gnani.ai uses TensorRT-LLM, Triton Inference Server and Riva NIM microservices to optimize its AI for virtual customer service assistants and speech analytics.

Large enterprises building LLMs with NeMo include:

• Flipkart, a major Indian ecommerce company majority-owned by Walmart, is integrating NeMo Guardrails, an open-source toolkit that enables developers to add programmable guardrails to LLMs, to enhance the safety of its conversational AI systems.
• Krutrim, part of the Ola Group of businesses that includes one of India’s top ride-booking platforms, is developing a multilingual Indic foundation model using Mistral NeMo 12B, a state-of-the-art LLM developed by Mistral AI and NVIDIA.
• Zoho Corporation, a global technology company based in Chennai, will use NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to optimize and deliver language models for its over 700,000 customers. The company will use NeMo running on NVIDIA Hopper GPUs to pretrain narrow, small, medium and large models from scratch for over 100 business applications.

India’s top global systems integrators are also offering NVIDIA NeMo-accelerated solutions to their customers.

• Infosys will work on specific tools and solutions using the NVIDIA AI stack. The company’s center of excellence is also developing AI-powered small language models that will be offered to customers as a service.
• Tata Consultancy Services has developed AI solutions based on NVIDIA NIM Agent Blueprints for the telecommunications, retail, manufacturing, automotive and financial services industries. TCS’ offerings include NeMo-powered, domain-specific language models that can be customized to address customer queries and answer company-specific questions for employees for all enterprise functions such as IT, HR or field operations.
• Wipro is using NVIDIA AI Enterprise software including NIM Agent Blueprints and NeMo to help businesses easily develop custom conversational AI solutions such as digital humans to support customer service interactions.

Wipro and TCS also use NeMo Curator’s synthetic data generation pipelines to generate data in languages other than English to customize LLMs for their clients.

To learn more about NVIDIA’s collaboration with businesses and developers in India, watch the replay of company founder and CEO Jensen Huang’s fireside chat at the NVIDIA AI Summit.

Read More

Namaste, vanakkam, sat sri akaal — these are just three forms of greeting in India, a country with 22 constitutionally recognized languages and over 1,500 more recorded by the country’s census. Around 10% of its residents speak English, the internet’s most common language.

As India, the world’s most populous country, forges ahead with rapid digitalization efforts, its enterprises and local startups are developing multilingual AI models that enable more Indians to interact with technology in their primary language. It’s a case study in sovereign AI — the development of domestic AI infrastructure that is built on local datasets and reflects a region’s specific dialects, cultures and practices.

These projects are building language models for Indic languages and English that can power customer service AI agents for businesses, rapidly translate content to broaden access to information, and enable services to more easily reach a diverse population of over 1.4 billion individuals.

To support initiatives like these, NVIDIA has released a small language model for Hindi, India’s most prevalent language with over half a billion speakers. Now available as an NVIDIA NIM microservice, the model, dubbed Nemotron-4-Mini-Hindi-4B, can be easily deployed on any NVIDIA GPU-accelerated system for optimized performance.

Tech Mahindra, an Indian IT services and consulting company, is the first to use the Nemotron Hindi NIM microservice to develop an AI model called Indus 2.0, which is focused on Hindi and dozens of its dialects. Indus 2.0 harnesses Tech Mahindra’s high-quality fine-tuning data to further boost model accuracy, unlocking opportunities for clients in banking, education, healthcare and other industries to deliver localized services.

Tech Mahindra will showcase Indus 2.0 at the NVIDIA AI Summit, taking place Oct. 23-25 in Mumbai. The company also uses NVIDIA NeMo to develop its sovereign large language model (LLM) platform, TeNo.

NVIDIA NIM Makes AI Adoption for Hindi as Easy as Ek, Do, Teen

The Nemotron Hindi model has 4 billion parameters and is derived from Nemotron-4 15B, a 15-billion parameter multilingual language model developed by NVIDIA. The model was pruned, distilled and trained with a combination of real-world Hindi data, synthetic Hindi data and an equal amount of English data using NVIDIA NeMo, an end-to-end, cloud-native framework and suite of microservices for developing generative AI.

The dataset was created with NVIDIA NeMo Curator, which improves generative AI model accuracy by processing high-quality multimodal data at scale for training and customization. NeMo Curator uses NVIDIA RAPIDS libraries to accelerate data processing pipelines on multi-node GPU systems, lowering processing time and total cost of ownership. It also provides pre-built pipelines and building blocks for synthetic data generation, data filtering, classification and deduplication to process high-quality data.

After fine-tuning with NeMo, the final model leads on multiple accuracy benchmarks for AI models with up to 8 billion parameters. Packaged as a NIM microservice, it can be easily harnessed to support use cases across industries such as education, retail and healthcare.

It’s available as part of the NVIDIA AI Enterprise software platform, which gives businesses access to additional resources, including technical support and enterprise-grade security, to streamline AI development for production environments.

Bevy of Businesses Serves Multilingual Population

Innovators, major enterprises and global systems integrators across India are building customized language models using NVIDIA NeMo.

Companies in the NVIDIA Inception program for cutting-edge startups are using NeMo to develop AI models for several Indic languages.

Sarvam AI offers enterprise customers speech-to-text, text-to-speech, translation and data parsing models. The company developed Sarvam 1, India’s first homegrown, multilingual LLM, which was trained from scratch on domestic AI infrastructure powered by NVIDIA H100 Tensor Core GPUs.

Sarvam 1 — developed using NVIDIA AI Enterprise software including NeMo Curator and NeMo Framework — supports English and 10 major Indian languages, including Bengali, Marathi, Tamil and Telugu.

Sarvam AI also uses NVIDIA NIM microservices, NVIDIA Riva for conversational AI, NVIDIA TensorRT-LLM software and NVIDIA Triton Inference Server to optimize and deploy conversational AI agents with sub-second latency.

Another Inception startup, Gnani.ai, built a multilingual speech-to-speech LLM that powers AI customer service assistants that handle around 10 million real-time voice interactions daily for over 150 banking, insurance and financial services companies across India and the U.S. The model supports 14 languages and was trained on over 14 million hours of conversational speech data using NVIDIA Hopper GPUs and NeMo Framework.

Gnani.ai uses TensorRT-LLM, Triton Inference Server and Riva NIM microservices to optimize its AI for virtual customer service assistants and speech analytics.

Large enterprises building LLMs with NeMo include:

• Flipkart, a major Indian ecommerce company majority-owned by Walmart, is integrating NeMo Guardrails, an open-source toolkit that enables developers to add programmable guardrails to LLMs, to enhance the safety of its conversational AI systems.
• Krutrim, part of the Ola Group of businesses that includes one of India’s top ride-booking platforms, is developing a multilingual Indic foundation model using Mistral NeMo 12B, a state-of-the-art LLM developed by Mistral AI and NVIDIA.
• Zoho Corporation, a global technology company based in Chennai, will use NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to optimize and deliver language models for its over 700,000 customers. The company will use NeMo running on NVIDIA Hopper GPUs to pretrain narrow, small, medium and large models from scratch for over 100 business applications.

India’s top global systems integrators are also offering NVIDIA NeMo-accelerated solutions to their customers.

• Infosys will work on specific tools and solutions using the NVIDIA AI stack. The company’s center of excellence is also developing AI-powered small language models that will be offered to customers as a service.
• Tata Consultancy Services has developed AI solutions based on NVIDIA NIM Agent Blueprints for the telecommunications, retail, manufacturing, automotive and financial services industries. TCS’ offerings include NeMo-powered, domain-specific language models that can be customized to address customer queries and answer company-specific questions for employees for all enterprise functions such as IT, HR or field operations.
• Wipro is using NVIDIA AI Enterprise software including NIM Agent Blueprints and NeMo to help businesses easily develop custom conversational AI solutions such as digital humans to support customer service interactions.

Wipro and TCS also use NeMo Curator’s synthetic data generation pipelines to generate data in languages other than English to customize LLMs for their clients.

To learn more about NVIDIA’s collaboration with businesses and developers in India, watch the replay of company founder and CEO Jensen Huang’s fireside chat at the NVIDIA AI Summit.

Read More

Namaste, vanakkam, sat sri akaal — these are just three forms of greeting in India, a country with 22 constitutionally recognized languages and over 1,500 more recorded by the country’s census. Around 10% of its residents speak English, the internet’s most common language.

As India, the world’s most populous country, forges ahead with rapid digitalization efforts, its enterprises and local startups are developing multilingual AI models that enable more Indians to interact with technology in their primary language. It’s a case study in sovereign AI — the development of domestic AI infrastructure that is built on local datasets and reflects a region’s specific dialects, cultures and practices.

These projects are building language models for Indic languages and English that can power customer service AI agents for businesses, rapidly translate content to broaden access to information, and enable services to more easily reach a diverse population of over 1.4 billion individuals.

To support initiatives like these, NVIDIA has released a small language model for Hindi, India’s most prevalent language with over half a billion speakers. Now available as an NVIDIA NIM microservice, the model, dubbed Nemotron-4-Mini-Hindi-4B, can be easily deployed on any NVIDIA GPU-accelerated system for optimized performance.

Tech Mahindra, an Indian IT services and consulting company, is the first to use the Nemotron Hindi NIM microservice to develop an AI model called Indus 2.0, which is focused on Hindi and dozens of its dialects. Indus 2.0 harnesses Tech Mahindra’s high-quality fine-tuning data to further boost model accuracy, unlocking opportunities for clients in banking, education, healthcare and other industries to deliver localized services.

Tech Mahindra will showcase Indus 2.0 at the NVIDIA AI Summit, taking place Oct. 23-25 in Mumbai. The company also uses NVIDIA NeMo to develop its sovereign large language model (LLM) platform, TeNo.

NVIDIA NIM Makes AI Adoption for Hindi as Easy as Ek, Do, Teen

The Nemotron Hindi model has 4 billion parameters and is derived from Nemotron-4 15B, a 15-billion parameter multilingual language model developed by NVIDIA. The model was pruned, distilled and trained with a combination of real-world Hindi data, synthetic Hindi data and an equal amount of English data using NVIDIA NeMo, an end-to-end, cloud-native framework and suite of microservices for developing generative AI.

The dataset was created with NVIDIA NeMo Curator, which improves generative AI model accuracy by processing high-quality multimodal data at scale for training and customization. NeMo Curator uses NVIDIA RAPIDS libraries to accelerate data processing pipelines on multi-node GPU systems, lowering processing time and total cost of ownership. It also provides pre-built pipelines and building blocks for synthetic data generation, data filtering, classification and deduplication to process high-quality data.

After fine-tuning with NeMo, the final model leads on multiple accuracy benchmarks for AI models with up to 8 billion parameters. Packaged as a NIM microservice, it can be easily harnessed to support use cases across industries such as education, retail and healthcare.

It’s available as part of the NVIDIA AI Enterprise software platform, which gives businesses access to additional resources, including technical support and enterprise-grade security, to streamline AI development for production environments.

Bevy of Businesses Serves Multilingual Population

Innovators, major enterprises and global systems integrators across India are building customized language models using NVIDIA NeMo.

Companies in the NVIDIA Inception program for cutting-edge startups are using NeMo to develop AI models for several Indic languages.

Sarvam AI offers enterprise customers speech-to-text, text-to-speech, translation and data parsing models. The company developed Sarvam 1, India’s first homegrown, multilingual LLM, which was trained from scratch on domestic AI infrastructure powered by NVIDIA H100 Tensor Core GPUs.

Sarvam 1 — developed using NVIDIA AI Enterprise software including NeMo Curator and NeMo Framework — supports English and 10 major Indian languages, including Bengali, Marathi, Tamil and Telugu.

Sarvam AI also uses NVIDIA NIM microservices, NVIDIA Riva for conversational AI, NVIDIA TensorRT-LLM software and NVIDIA Triton Inference Server to optimize and deploy conversational AI agents with sub-second latency.

Another Inception startup, Gnani.ai, built a multilingual speech-to-speech LLM that powers AI customer service assistants that handle around 10 million real-time voice interactions daily for over 150 banking, insurance and financial services companies across India and the U.S. The model supports 14 languages and was trained on over 14 million hours of conversational speech data using NVIDIA Hopper GPUs and NeMo Framework.

Gnani.ai uses TensorRT-LLM, Triton Inference Server and Riva NIM microservices to optimize its AI for virtual customer service assistants and speech analytics.

Large enterprises building LLMs with NeMo include:

• Flipkart, a major Indian ecommerce company majority-owned by Walmart, is integrating NeMo Guardrails, an open-source toolkit that enables developers to add programmable guardrails to LLMs, to enhance the safety of its conversational AI systems.
• Krutrim, part of the Ola Group of businesses that includes one of India’s top ride-booking platforms, is developing a multilingual Indic foundation model using Mistral NeMo 12B, a state-of-the-art LLM developed by Mistral AI and NVIDIA.
• Zoho Corporation, a global technology company based in Chennai, will use NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to optimize and deliver language models for its over 700,000 customers. The company will use NeMo running on NVIDIA Hopper GPUs to pretrain narrow, small, medium and large models from scratch for over 100 business applications.

India’s top global systems integrators are also offering NVIDIA NeMo-accelerated solutions to their customers.

• Infosys will work on specific tools and solutions using the NVIDIA AI stack. The company’s center of excellence is also developing AI-powered small language models that will be offered to customers as a service.
• Tata Consultancy Services has developed AI solutions based on NVIDIA NIM Agent Blueprints for the telecommunications, retail, manufacturing, automotive and financial services industries. TCS’ offerings include NeMo-powered, domain-specific language models that can be customized to address customer queries and answer company-specific questions for employees for all enterprise functions such as IT, HR or field operations.
• Wipro is using NVIDIA AI Enterprise software including NIM Agent Blueprints and NeMo to help businesses easily develop custom conversational AI solutions such as digital humans to support customer service interactions.

Wipro and TCS also use NeMo Curator’s synthetic data generation pipelines to generate data in languages other than English to customize LLMs for their clients.

To learn more about NVIDIA’s collaboration with businesses and developers in India, watch the replay of company founder and CEO Jensen Huang’s fireside chat at the NVIDIA AI Summit.

Read More

Namaste, vanakkam, sat sri akaal — these are just three forms of greeting in India, a country with 22 constitutionally recognized languages and over 1,500 more recorded by the country’s census. Around 10% of its residents speak English, the internet’s most common language.

As India, the world’s most populous country, forges ahead with rapid digitalization efforts, its enterprises and local startups are developing multilingual AI models that enable more Indians to interact with technology in their primary language. It’s a case study in sovereign AI — the development of domestic AI infrastructure that is built on local datasets and reflects a region’s specific dialects, cultures and practices.

These projects are building language models for Indic languages and English that can power customer service AI agents for businesses, rapidly translate content to broaden access to information, and enable services to more easily reach a diverse population of over 1.4 billion individuals.

To support initiatives like these, NVIDIA has released a small language model for Hindi, India’s most prevalent language with over half a billion speakers. Now available as an NVIDIA NIM microservice, the model, dubbed Nemotron-4-Mini-Hindi-4B, can be easily deployed on any NVIDIA GPU-accelerated system for optimized performance.

Tech Mahindra, an Indian IT services and consulting company, is the first to use the Nemotron Hindi NIM microservice to develop an AI model called Indus 2.0, which is focused on Hindi and dozens of its dialects. Indus 2.0 harnesses Tech Mahindra’s high-quality fine-tuning data to further boost model accuracy, unlocking opportunities for clients in banking, education, healthcare and other industries to deliver localized services.

Tech Mahindra will showcase Indus 2.0 at the NVIDIA AI Summit, taking place Oct. 23-25 in Mumbai. The company also uses NVIDIA NeMo to develop its sovereign large language model (LLM) platform, TeNo.

NVIDIA NIM Makes AI Adoption for Hindi as Easy as Ek, Do, Teen

The Nemotron Hindi model has 4 billion parameters and is derived from Nemotron-4 15B, a 15-billion parameter multilingual language model developed by NVIDIA. The model was pruned, distilled and trained with a combination of real-world Hindi data, synthetic Hindi data and an equal amount of English data using NVIDIA NeMo, an end-to-end, cloud-native framework and suite of microservices for developing generative AI.

The dataset was created with NVIDIA NeMo Curator, which improves generative AI model accuracy by processing high-quality multimodal data at scale for training and customization. NeMo Curator uses NVIDIA RAPIDS libraries to accelerate data processing pipelines on multi-node GPU systems, lowering processing time and total cost of ownership. It also provides pre-built pipelines and building blocks for synthetic data generation, data filtering, classification and deduplication to process high-quality data.

After fine-tuning with NeMo, the final model leads on multiple accuracy benchmarks for AI models with up to 8 billion parameters. Packaged as a NIM microservice, it can be easily harnessed to support use cases across industries such as education, retail and healthcare.

It’s available as part of the NVIDIA AI Enterprise software platform, which gives businesses access to additional resources, including technical support and enterprise-grade security, to streamline AI development for production environments.

Bevy of Businesses Serves Multilingual Population

Innovators, major enterprises and global systems integrators across India are building customized language models using NVIDIA NeMo.

Companies in the NVIDIA Inception program for cutting-edge startups are using NeMo to develop AI models for several Indic languages.

Sarvam AI offers enterprise customers speech-to-text, text-to-speech, translation and data parsing models. The company developed Sarvam 1, India’s first homegrown, multilingual LLM, which was trained from scratch on domestic AI infrastructure powered by NVIDIA H100 Tensor Core GPUs.

Sarvam 1 — developed using NVIDIA AI Enterprise software including NeMo Curator and NeMo Framework — supports English and 10 major Indian languages, including Bengali, Marathi, Tamil and Telugu.

Sarvam AI also uses NVIDIA NIM microservices, NVIDIA Riva for conversational AI, NVIDIA TensorRT-LLM software and NVIDIA Triton Inference Server to optimize and deploy conversational AI agents with sub-second latency.

Another Inception startup, Gnani.ai, built a multilingual speech-to-speech LLM that powers AI customer service assistants that handle around 10 million real-time voice interactions daily for over 150 banking, insurance and financial services companies across India and the U.S. The model supports 14 languages and was trained on over 14 million hours of conversational speech data using NVIDIA Hopper GPUs and NeMo Framework.

Gnani.ai uses TensorRT-LLM, Triton Inference Server and Riva NIM microservices to optimize its AI for virtual customer service assistants and speech analytics.

Large enterprises building LLMs with NeMo include:

• Flipkart, a major Indian ecommerce company majority-owned by Walmart, is integrating NeMo Guardrails, an open-source toolkit that enables developers to add programmable guardrails to LLMs, to enhance the safety of its conversational AI systems.
• Krutrim, part of the Ola Group of businesses that includes one of India’s top ride-booking platforms, is developing a multilingual Indic foundation model using Mistral NeMo 12B, a state-of-the-art LLM developed by Mistral AI and NVIDIA.
• Zoho Corporation, a global technology company based in Chennai, will use NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to optimize and deliver language models for its over 700,000 customers. The company will use NeMo running on NVIDIA Hopper GPUs to pretrain narrow, small, medium and large models from scratch for over 100 business applications.

India’s top global systems integrators are also offering NVIDIA NeMo-accelerated solutions to their customers.

• Infosys will work on specific tools and solutions using the NVIDIA AI stack. The company’s center of excellence is also developing AI-powered small language models that will be offered to customers as a service.
• Tata Consultancy Services has developed AI solutions based on NVIDIA NIM Agent Blueprints for the telecommunications, retail, manufacturing, automotive and financial services industries. TCS’ offerings include NeMo-powered, domain-specific language models that can be customized to address customer queries and answer company-specific questions for employees for all enterprise functions such as IT, HR or field operations.
• Wipro is using NVIDIA AI Enterprise software including NIM Agent Blueprints and NeMo to help businesses easily develop custom conversational AI solutions such as digital humans to support customer service interactions.

Wipro and TCS also use NeMo Curator’s synthetic data generation pipelines to generate data in languages other than English to customize LLMs for their clients.

To learn more about NVIDIA’s collaboration with businesses and developers in India, watch the replay of company founder and CEO Jensen Huang’s fireside chat at the NVIDIA AI Summit.

Read More

Namaste, vanakkam, sat sri akaal — these are just three forms of greeting in India, a country with 22 constitutionally recognized languages and over 1,500 more recorded by the country’s census. Around 10% of its residents speak English, the internet’s most common language.

As India, the world’s most populous country, forges ahead with rapid digitalization efforts, its enterprises and local startups are developing multilingual AI models that enable more Indians to interact with technology in their primary language. It’s a case study in sovereign AI — the development of domestic AI infrastructure that is built on local datasets and reflects a region’s specific dialects, cultures and practices.

These projects are building language models for Indic languages and English that can power customer service AI agents for businesses, rapidly translate content to broaden access to information, and enable services to more easily reach a diverse population of over 1.4 billion individuals.

To support initiatives like these, NVIDIA has released a small language model for Hindi, India’s most prevalent language with over half a billion speakers. Now available as an NVIDIA NIM microservice, the model, dubbed Nemotron-4-Mini-Hindi-4B, can be easily deployed on any NVIDIA GPU-accelerated system for optimized performance.

Tech Mahindra, an Indian IT services and consulting company, is the first to use the Nemotron Hindi NIM microservice to develop an AI model called Indus 2.0, which is focused on Hindi and dozens of its dialects. Indus 2.0 harnesses Tech Mahindra’s high-quality fine-tuning data to further boost model accuracy, unlocking opportunities for clients in banking, education, healthcare and other industries to deliver localized services.

Tech Mahindra will showcase Indus 2.0 at the NVIDIA AI Summit, taking place Oct. 23-25 in Mumbai. The company also uses NVIDIA NeMo to develop its sovereign large language model (LLM) platform, TeNo.

NVIDIA NIM Makes AI Adoption for Hindi as Easy as Ek, Do, Teen

The Nemotron Hindi model has 4 billion parameters and is derived from Nemotron-4 15B, a 15-billion parameter multilingual language model developed by NVIDIA. The model was pruned, distilled and trained with a combination of real-world Hindi data, synthetic Hindi data and an equal amount of English data using NVIDIA NeMo, an end-to-end, cloud-native framework and suite of microservices for developing generative AI.

The dataset was created with NVIDIA NeMo Curator, which improves generative AI model accuracy by processing high-quality multimodal data at scale for training and customization. NeMo Curator uses NVIDIA RAPIDS libraries to accelerate data processing pipelines on multi-node GPU systems, lowering processing time and total cost of ownership. It also provides pre-built pipelines and building blocks for synthetic data generation, data filtering, classification and deduplication to process high-quality data.

After fine-tuning with NeMo, the final model leads on multiple accuracy benchmarks for AI models with up to 8 billion parameters. Packaged as a NIM microservice, it can be easily harnessed to support use cases across industries such as education, retail and healthcare.

It’s available as part of the NVIDIA AI Enterprise software platform, which gives businesses access to additional resources, including technical support and enterprise-grade security, to streamline AI development for production environments.

Bevy of Businesses Serves Multilingual Population

Innovators, major enterprises and global systems integrators across India are building customized language models using NVIDIA NeMo.

Companies in the NVIDIA Inception program for cutting-edge startups are using NeMo to develop AI models for several Indic languages.

Sarvam AI offers enterprise customers speech-to-text, text-to-speech, translation and data parsing models. The company developed Sarvam 1, India’s first homegrown, multilingual LLM, which was trained from scratch on domestic AI infrastructure powered by NVIDIA H100 Tensor Core GPUs.

Sarvam 1 — developed using NVIDIA AI Enterprise software including NeMo Curator and NeMo Framework — supports English and 10 major Indian languages, including Bengali, Marathi, Tamil and Telugu.

Sarvam AI also uses NVIDIA NIM microservices, NVIDIA Riva for conversational AI, NVIDIA TensorRT-LLM software and NVIDIA Triton Inference Server to optimize and deploy conversational AI agents with sub-second latency.

Another Inception startup, Gnani.ai, built a multilingual speech-to-speech LLM that powers AI customer service assistants that handle around 10 million real-time voice interactions daily for over 150 banking, insurance and financial services companies across India and the U.S. The model supports 14 languages and was trained on over 14 million hours of conversational speech data using NVIDIA Hopper GPUs and NeMo Framework.

Gnani.ai uses TensorRT-LLM, Triton Inference Server and Riva NIM microservices to optimize its AI for virtual customer service assistants and speech analytics.

Large enterprises building LLMs with NeMo include:

• Flipkart, a major Indian ecommerce company majority-owned by Walmart, is integrating NeMo Guardrails, an open-source toolkit that enables developers to add programmable guardrails to LLMs, to enhance the safety of its conversational AI systems.
• Krutrim, part of the Ola Group of businesses that includes one of India’s top ride-booking platforms, is developing a multilingual Indic foundation model using Mistral NeMo 12B, a state-of-the-art LLM developed by Mistral AI and NVIDIA.
• Zoho Corporation, a global technology company based in Chennai, will use NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to optimize and deliver language models for its over 700,000 customers. The company will use NeMo running on NVIDIA Hopper GPUs to pretrain narrow, small, medium and large models from scratch for over 100 business applications.

India’s top global systems integrators are also offering NVIDIA NeMo-accelerated solutions to their customers.

• Infosys will work on specific tools and solutions using the NVIDIA AI stack. The company’s center of excellence is also developing AI-powered small language models that will be offered to customers as a service.
• Tata Consultancy Services has developed AI solutions based on NVIDIA NIM Agent Blueprints for the telecommunications, retail, manufacturing, automotive and financial services industries. TCS’ offerings include NeMo-powered, domain-specific language models that can be customized to address customer queries and answer company-specific questions for employees for all enterprise functions such as IT, HR or field operations.
• Wipro is using NVIDIA AI Enterprise software including NIM Agent Blueprints and NeMo to help businesses easily develop custom conversational AI solutions such as digital humans to support customer service interactions.

Wipro and TCS also use NeMo Curator’s synthetic data generation pipelines to generate data in languages other than English to customize LLMs for their clients.

To learn more about NVIDIA’s collaboration with businesses and developers in India, watch the replay of company founder and CEO Jensen Huang’s fireside chat at the NVIDIA AI Summit.

Read More

Namaste, vanakkam, sat sri akaal — these are just three forms of greeting in India, a country with 22 constitutionally recognized languages and over 1,500 more recorded by the country’s census. Around 10% of its residents speak English, the internet’s most common language.

As India, the world’s most populous country, forges ahead with rapid digitalization efforts, its enterprises and local startups are developing multilingual AI models that enable more Indians to interact with technology in their primary language. It’s a case study in sovereign AI — the development of domestic AI infrastructure that is built on local datasets and reflects a region’s specific dialects, cultures and practices.

These projects are building language models for Indic languages and English that can power customer service AI agents for businesses, rapidly translate content to broaden access to information, and enable services to more easily reach a diverse population of over 1.4 billion individuals.

To support initiatives like these, NVIDIA has released a small language model for Hindi, India’s most prevalent language with over half a billion speakers. Now available as an NVIDIA NIM microservice, the model, dubbed Nemotron-4-Mini-Hindi-4B, can be easily deployed on any NVIDIA GPU-accelerated system for optimized performance.

Tech Mahindra, an Indian IT services and consulting company, is the first to use the Nemotron Hindi NIM microservice to develop an AI model called Indus 2.0, which is focused on Hindi and dozens of its dialects. Indus 2.0 harnesses Tech Mahindra’s high-quality fine-tuning data to further boost model accuracy, unlocking opportunities for clients in banking, education, healthcare and other industries to deliver localized services.

Tech Mahindra will showcase Indus 2.0 at the NVIDIA AI Summit, taking place Oct. 23-25 in Mumbai. The company also uses NVIDIA NeMo to develop its sovereign large language model (LLM) platform, TeNo.

NVIDIA NIM Makes AI Adoption for Hindi as Easy as Ek, Do, Teen

The Nemotron Hindi model has 4 billion parameters and is derived from Nemotron-4 15B, a 15-billion parameter multilingual language model developed by NVIDIA. The model was pruned, distilled and trained with a combination of real-world Hindi data, synthetic Hindi data and an equal amount of English data using NVIDIA NeMo, an end-to-end, cloud-native framework and suite of microservices for developing generative AI.

The dataset was created with NVIDIA NeMo Curator, which improves generative AI model accuracy by processing high-quality multimodal data at scale for training and customization. NeMo Curator uses NVIDIA RAPIDS libraries to accelerate data processing pipelines on multi-node GPU systems, lowering processing time and total cost of ownership. It also provides pre-built pipelines and building blocks for synthetic data generation, data filtering, classification and deduplication to process high-quality data.

After fine-tuning with NeMo, the final model leads on multiple accuracy benchmarks for AI models with up to 8 billion parameters. Packaged as a NIM microservice, it can be easily harnessed to support use cases across industries such as education, retail and healthcare.

It’s available as part of the NVIDIA AI Enterprise software platform, which gives businesses access to additional resources, including technical support and enterprise-grade security, to streamline AI development for production environments.

Bevy of Businesses Serves Multilingual Population

Innovators, major enterprises and global systems integrators across India are building customized language models using NVIDIA NeMo.

Companies in the NVIDIA Inception program for cutting-edge startups are using NeMo to develop AI models for several Indic languages.

Sarvam AI offers enterprise customers speech-to-text, text-to-speech, translation and data parsing models. The company developed Sarvam 1, India’s first homegrown, multilingual LLM, which was trained from scratch on domestic AI infrastructure powered by NVIDIA H100 Tensor Core GPUs.

Sarvam 1 — developed using NVIDIA AI Enterprise software including NeMo Curator and NeMo Framework — supports English and 10 major Indian languages, including Bengali, Marathi, Tamil and Telugu.

Sarvam AI also uses NVIDIA NIM microservices, NVIDIA Riva for conversational AI, NVIDIA TensorRT-LLM software and NVIDIA Triton Inference Server to optimize and deploy conversational AI agents with sub-second latency.

Another Inception startup, Gnani.ai, built a multilingual speech-to-speech LLM that powers AI customer service assistants that handle around 10 million real-time voice interactions daily for over 150 banking, insurance and financial services companies across India and the U.S. The model supports 14 languages and was trained on over 14 million hours of conversational speech data using NVIDIA Hopper GPUs and NeMo Framework.

Gnani.ai uses TensorRT-LLM, Triton Inference Server and Riva NIM microservices to optimize its AI for virtual customer service assistants and speech analytics.

Large enterprises building LLMs with NeMo include:

• Flipkart, a major Indian ecommerce company majority-owned by Walmart, is integrating NeMo Guardrails, an open-source toolkit that enables developers to add programmable guardrails to LLMs, to enhance the safety of its conversational AI systems.
• Krutrim, part of the Ola Group of businesses that includes one of India’s top ride-booking platforms, is developing a multilingual Indic foundation model using Mistral NeMo 12B, a state-of-the-art LLM developed by Mistral AI and NVIDIA.
• Zoho Corporation, a global technology company based in Chennai, will use NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to optimize and deliver language models for its over 700,000 customers. The company will use NeMo running on NVIDIA Hopper GPUs to pretrain narrow, small, medium and large models from scratch for over 100 business applications.

India’s top global systems integrators are also offering NVIDIA NeMo-accelerated solutions to their customers.

• Infosys will work on specific tools and solutions using the NVIDIA AI stack. The company’s center of excellence is also developing AI-powered small language models that will be offered to customers as a service.
• Tata Consultancy Services has developed AI solutions based on NVIDIA NIM Agent Blueprints for the telecommunications, retail, manufacturing, automotive and financial services industries. TCS’ offerings include NeMo-powered, domain-specific language models that can be customized to address customer queries and answer company-specific questions for employees for all enterprise functions such as IT, HR or field operations.
• Wipro is using NVIDIA AI Enterprise software including NIM Agent Blueprints and NeMo to help businesses easily develop custom conversational AI solutions such as digital humans to support customer service interactions.

Wipro and TCS also use NeMo Curator’s synthetic data generation pipelines to generate data in languages other than English to customize LLMs for their clients.

To learn more about NVIDIA’s collaboration with businesses and developers in India, watch the replay of company founder and CEO Jensen Huang’s fireside chat at the NVIDIA AI Summit.

Read More

Namaste, vanakkam, sat sri akaal — these are just three forms of greeting in India, a country with 22 constitutionally recognized languages and over 1,500 more recorded by the country’s census. Around 10% of its residents speak English, the internet’s most common language.

As India, the world’s most populous country, forges ahead with rapid digitalization efforts, its enterprises and local startups are developing multilingual AI models that enable more Indians to interact with technology in their primary language. It’s a case study in sovereign AI — the development of domestic AI infrastructure that is built on local datasets and reflects a region’s specific dialects, cultures and practices.

These projects are building language models for Indic languages and English that can power customer service AI agents for businesses, rapidly translate content to broaden access to information, and enable services to more easily reach a diverse population of over 1.4 billion individuals.

To support initiatives like these, NVIDIA has released a small language model for Hindi, India’s most prevalent language with over half a billion speakers. Now available as an NVIDIA NIM microservice, the model, dubbed Nemotron-4-Mini-Hindi-4B, can be easily deployed on any NVIDIA GPU-accelerated system for optimized performance.

Tech Mahindra, an Indian IT services and consulting company, is the first to use the Nemotron Hindi NIM microservice to develop an AI model called Indus 2.0, which is focused on Hindi and dozens of its dialects. Indus 2.0 harnesses Tech Mahindra’s high-quality fine-tuning data to further boost model accuracy, unlocking opportunities for clients in banking, education, healthcare and other industries to deliver localized services.

Tech Mahindra will showcase Indus 2.0 at the NVIDIA AI Summit, taking place Oct. 23-25 in Mumbai. The company also uses NVIDIA NeMo to develop its sovereign large language model (LLM) platform, TeNo.

NVIDIA NIM Makes AI Adoption for Hindi as Easy as Ek, Do, Teen

The Nemotron Hindi model has 4 billion parameters and is derived from Nemotron-4 15B, a 15-billion parameter multilingual language model developed by NVIDIA. The model was pruned, distilled and trained with a combination of real-world Hindi data, synthetic Hindi data and an equal amount of English data using NVIDIA NeMo, an end-to-end, cloud-native framework and suite of microservices for developing generative AI.

The dataset was created with NVIDIA NeMo Curator, which improves generative AI model accuracy by processing high-quality multimodal data at scale for training and customization. NeMo Curator uses NVIDIA RAPIDS libraries to accelerate data processing pipelines on multi-node GPU systems, lowering processing time and total cost of ownership. It also provides pre-built pipelines and building blocks for synthetic data generation, data filtering, classification and deduplication to process high-quality data.

After fine-tuning with NeMo, the final model leads on multiple accuracy benchmarks for AI models with up to 8 billion parameters. Packaged as a NIM microservice, it can be easily harnessed to support use cases across industries such as education, retail and healthcare.

It’s available as part of the NVIDIA AI Enterprise software platform, which gives businesses access to additional resources, including technical support and enterprise-grade security, to streamline AI development for production environments.

Bevy of Businesses Serves Multilingual Population

Innovators, major enterprises and global systems integrators across India are building customized language models using NVIDIA NeMo.

Companies in the NVIDIA Inception program for cutting-edge startups are using NeMo to develop AI models for several Indic languages.

Sarvam AI offers enterprise customers speech-to-text, text-to-speech, translation and data parsing models. The company developed Sarvam 1, India’s first homegrown, multilingual LLM, which was trained from scratch on domestic AI infrastructure powered by NVIDIA H100 Tensor Core GPUs.

Sarvam 1 — developed using NVIDIA AI Enterprise software including NeMo Curator and NeMo Framework — supports English and 10 major Indian languages, including Bengali, Marathi, Tamil and Telugu.

Sarvam AI also uses NVIDIA NIM microservices, NVIDIA Riva for conversational AI, NVIDIA TensorRT-LLM software and NVIDIA Triton Inference Server to optimize and deploy conversational AI agents with sub-second latency.

Another Inception startup, Gnani.ai, built a multilingual speech-to-speech LLM that powers AI customer service assistants that handle around 10 million real-time voice interactions daily for over 150 banking, insurance and financial services companies across India and the U.S. The model supports 14 languages and was trained on over 14 million hours of conversational speech data using NVIDIA Hopper GPUs and NeMo Framework.

Gnani.ai uses TensorRT-LLM, Triton Inference Server and Riva NIM microservices to optimize its AI for virtual customer service assistants and speech analytics.

Large enterprises building LLMs with NeMo include:

• Flipkart, a major Indian ecommerce company majority-owned by Walmart, is integrating NeMo Guardrails, an open-source toolkit that enables developers to add programmable guardrails to LLMs, to enhance the safety of its conversational AI systems.
• Krutrim, part of the Ola Group of businesses that includes one of India’s top ride-booking platforms, is developing a multilingual Indic foundation model using Mistral NeMo 12B, a state-of-the-art LLM developed by Mistral AI and NVIDIA.
• Zoho Corporation, a global technology company based in Chennai, will use NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to optimize and deliver language models for its over 700,000 customers. The company will use NeMo running on NVIDIA Hopper GPUs to pretrain narrow, small, medium and large models from scratch for over 100 business applications.

India’s top global systems integrators are also offering NVIDIA NeMo-accelerated solutions to their customers.

• Infosys will work on specific tools and solutions using the NVIDIA AI stack. The company’s center of excellence is also developing AI-powered small language models that will be offered to customers as a service.
• Tata Consultancy Services has developed AI solutions based on NVIDIA NIM Agent Blueprints for the telecommunications, retail, manufacturing, automotive and financial services industries. TCS’ offerings include NeMo-powered, domain-specific language models that can be customized to address customer queries and answer company-specific questions for employees for all enterprise functions such as IT, HR or field operations.
• Wipro is using NVIDIA AI Enterprise software including NIM Agent Blueprints and NeMo to help businesses easily develop custom conversational AI solutions such as digital humans to support customer service interactions.

Wipro and TCS also use NeMo Curator’s synthetic data generation pipelines to generate data in languages other than English to customize LLMs for their clients.

To learn more about NVIDIA’s collaboration with businesses and developers in India, watch the replay of company founder and CEO Jensen Huang’s fireside chat at the NVIDIA AI Summit.

Read More

Namaste, vanakkam, sat sri akaal — these are just three forms of greeting in India, a country with 22 constitutionally recognized languages and over 1,500 more recorded by the country’s census. Around 10% of its residents speak English, the internet’s most common language.

As India, the world’s most populous country, forges ahead with rapid digitalization efforts, its enterprises and local startups are developing multilingual AI models that enable more Indians to interact with technology in their primary language. It’s a case study in sovereign AI — the development of domestic AI infrastructure that is built on local datasets and reflects a region’s specific dialects, cultures and practices.

These projects are building language models for Indic languages and English that can power customer service AI agents for businesses, rapidly translate content to broaden access to information, and enable services to more easily reach a diverse population of over 1.4 billion individuals.

To support initiatives like these, NVIDIA has released a small language model for Hindi, India’s most prevalent language with over half a billion speakers. Now available as an NVIDIA NIM microservice, the model, dubbed Nemotron-4-Mini-Hindi-4B, can be easily deployed on any NVIDIA GPU-accelerated system for optimized performance.

Tech Mahindra, an Indian IT services and consulting company, is the first to use the Nemotron Hindi NIM microservice to develop an AI model called Indus 2.0, which is focused on Hindi and dozens of its dialects. Indus 2.0 harnesses Tech Mahindra’s high-quality fine-tuning data to further boost model accuracy, unlocking opportunities for clients in banking, education, healthcare and other industries to deliver localized services.

Tech Mahindra will showcase Indus 2.0 at the NVIDIA AI Summit, taking place Oct. 23-25 in Mumbai. The company also uses NVIDIA NeMo to develop its sovereign large language model (LLM) platform, TeNo.

NVIDIA NIM Makes AI Adoption for Hindi as Easy as Ek, Do, Teen

The Nemotron Hindi model has 4 billion parameters and is derived from Nemotron-4 15B, a 15-billion parameter multilingual language model developed by NVIDIA. The model was pruned, distilled and trained with a combination of real-world Hindi data, synthetic Hindi data and an equal amount of English data using NVIDIA NeMo, an end-to-end, cloud-native framework and suite of microservices for developing generative AI.

The dataset was created with NVIDIA NeMo Curator, which improves generative AI model accuracy by processing high-quality multimodal data at scale for training and customization. NeMo Curator uses NVIDIA RAPIDS libraries to accelerate data processing pipelines on multi-node GPU systems, lowering processing time and total cost of ownership. It also provides pre-built pipelines and building blocks for synthetic data generation, data filtering, classification and deduplication to process high-quality data.

After fine-tuning with NeMo, the final model leads on multiple accuracy benchmarks for AI models with up to 8 billion parameters. Packaged as a NIM microservice, it can be easily harnessed to support use cases across industries such as education, retail and healthcare.

It’s available as part of the NVIDIA AI Enterprise software platform, which gives businesses access to additional resources, including technical support and enterprise-grade security, to streamline AI development for production environments.

Bevy of Businesses Serves Multilingual Population

Innovators, major enterprises and global systems integrators across India are building customized language models using NVIDIA NeMo.

Companies in the NVIDIA Inception program for cutting-edge startups are using NeMo to develop AI models for several Indic languages.

Sarvam AI offers enterprise customers speech-to-text, text-to-speech, translation and data parsing models. The company developed Sarvam 1, India’s first homegrown, multilingual LLM, which was trained from scratch on domestic AI infrastructure powered by NVIDIA H100 Tensor Core GPUs.

Sarvam 1 — developed using NVIDIA AI Enterprise software including NeMo Curator and NeMo Framework — supports English and 10 major Indian languages, including Bengali, Marathi, Tamil and Telugu.

Sarvam AI also uses NVIDIA NIM microservices, NVIDIA Riva for conversational AI, NVIDIA TensorRT-LLM software and NVIDIA Triton Inference Server to optimize and deploy conversational AI agents with sub-second latency.

Another Inception startup, Gnani.ai, built a multilingual speech-to-speech LLM that powers AI customer service assistants that handle around 10 million real-time voice interactions daily for over 150 banking, insurance and financial services companies across India and the U.S. The model supports 14 languages and was trained on over 14 million hours of conversational speech data using NVIDIA Hopper GPUs and NeMo Framework.

Gnani.ai uses TensorRT-LLM, Triton Inference Server and Riva NIM microservices to optimize its AI for virtual customer service assistants and speech analytics.

Large enterprises building LLMs with NeMo include:

• Flipkart, a major Indian ecommerce company majority-owned by Walmart, is integrating NeMo Guardrails, an open-source toolkit that enables developers to add programmable guardrails to LLMs, to enhance the safety of its conversational AI systems.
• Krutrim, part of the Ola Group of businesses that includes one of India’s top ride-booking platforms, is developing a multilingual Indic foundation model using Mistral NeMo 12B, a state-of-the-art LLM developed by Mistral AI and NVIDIA.
• Zoho Corporation, a global technology company based in Chennai, will use NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to optimize and deliver language models for its over 700,000 customers. The company will use NeMo running on NVIDIA Hopper GPUs to pretrain narrow, small, medium and large models from scratch for over 100 business applications.

India’s top global systems integrators are also offering NVIDIA NeMo-accelerated solutions to their customers.

• Infosys will work on specific tools and solutions using the NVIDIA AI stack. The company’s center of excellence is also developing AI-powered small language models that will be offered to customers as a service.
• Tata Consultancy Services has developed AI solutions based on NVIDIA NIM Agent Blueprints for the telecommunications, retail, manufacturing, automotive and financial services industries. TCS’ offerings include NeMo-powered, domain-specific language models that can be customized to address customer queries and answer company-specific questions for employees for all enterprise functions such as IT, HR or field operations.
• Wipro is using NVIDIA AI Enterprise software including NIM Agent Blueprints and NeMo to help businesses easily develop custom conversational AI solutions such as digital humans to support customer service interactions.

Wipro and TCS also use NeMo Curator’s synthetic data generation pipelines to generate data in languages other than English to customize LLMs for their clients.

To learn more about NVIDIA’s collaboration with businesses and developers in India, watch the replay of company founder and CEO Jensen Huang’s fireside chat at the NVIDIA AI Summit.

Read More

Namaste, vanakkam, sat sri akaal — these are just three forms of greeting in India, a country with 22 constitutionally recognized languages and over 1,500 more recorded by the country’s census. Around 10% of its residents speak English, the internet’s most common language.

As India, the world’s most populous country, forges ahead with rapid digitalization efforts, its enterprises and local startups are developing multilingual AI models that enable more Indians to interact with technology in their primary language. It’s a case study in sovereign AI — the development of domestic AI infrastructure that is built on local datasets and reflects a region’s specific dialects, cultures and practices.

These projects are building language models for Indic languages and English that can power customer service AI agents for businesses, rapidly translate content to broaden access to information, and enable services to more easily reach a diverse population of over 1.4 billion individuals.

To support initiatives like these, NVIDIA has released a small language model for Hindi, India’s most prevalent language with over half a billion speakers. Now available as an NVIDIA NIM microservice, the model, dubbed Nemotron-4-Mini-Hindi-4B, can be easily deployed on any NVIDIA GPU-accelerated system for optimized performance.

Tech Mahindra, an Indian IT services and consulting company, is the first to use the Nemotron Hindi NIM microservice to develop an AI model called Indus 2.0, which is focused on Hindi and dozens of its dialects. Indus 2.0 harnesses Tech Mahindra’s high-quality fine-tuning data to further boost model accuracy, unlocking opportunities for clients in banking, education, healthcare and other industries to deliver localized services.

Tech Mahindra will showcase Indus 2.0 at the NVIDIA AI Summit, taking place Oct. 23-25 in Mumbai. The company also uses NVIDIA NeMo to develop its sovereign large language model (LLM) platform, TeNo.

NVIDIA NIM Makes AI Adoption for Hindi as Easy as Ek, Do, Teen

The Nemotron Hindi model has 4 billion parameters and is derived from Nemotron-4 15B, a 15-billion parameter multilingual language model developed by NVIDIA. The model was pruned, distilled and trained with a combination of real-world Hindi data, synthetic Hindi data and an equal amount of English data using NVIDIA NeMo, an end-to-end, cloud-native framework and suite of microservices for developing generative AI.

The dataset was created with NVIDIA NeMo Curator, which improves generative AI model accuracy by processing high-quality multimodal data at scale for training and customization. NeMo Curator uses NVIDIA RAPIDS libraries to accelerate data processing pipelines on multi-node GPU systems, lowering processing time and total cost of ownership. It also provides pre-built pipelines and building blocks for synthetic data generation, data filtering, classification and deduplication to process high-quality data.

After fine-tuning with NeMo, the final model leads on multiple accuracy benchmarks for AI models with up to 8 billion parameters. Packaged as a NIM microservice, it can be easily harnessed to support use cases across industries such as education, retail and healthcare.

It’s available as part of the NVIDIA AI Enterprise software platform, which gives businesses access to additional resources, including technical support and enterprise-grade security, to streamline AI development for production environments.

Bevy of Businesses Serves Multilingual Population

Innovators, major enterprises and global systems integrators across India are building customized language models using NVIDIA NeMo.

Companies in the NVIDIA Inception program for cutting-edge startups are using NeMo to develop AI models for several Indic languages.

Sarvam AI offers enterprise customers speech-to-text, text-to-speech, translation and data parsing models. The company developed Sarvam 1, India’s first homegrown, multilingual LLM, which was trained from scratch on domestic AI infrastructure powered by NVIDIA H100 Tensor Core GPUs.

Sarvam 1 — developed using NVIDIA AI Enterprise software including NeMo Curator and NeMo Framework — supports English and 10 major Indian languages, including Bengali, Marathi, Tamil and Telugu.

Sarvam AI also uses NVIDIA NIM microservices, NVIDIA Riva for conversational AI, NVIDIA TensorRT-LLM software and NVIDIA Triton Inference Server to optimize and deploy conversational AI agents with sub-second latency.

Another Inception startup, Gnani.ai, built a multilingual speech-to-speech LLM that powers AI customer service assistants that handle around 10 million real-time voice interactions daily for over 150 banking, insurance and financial services companies across India and the U.S. The model supports 14 languages and was trained on over 14 million hours of conversational speech data using NVIDIA Hopper GPUs and NeMo Framework.

Gnani.ai uses TensorRT-LLM, Triton Inference Server and Riva NIM microservices to optimize its AI for virtual customer service assistants and speech analytics.

Large enterprises building LLMs with NeMo include:

• Flipkart, a major Indian ecommerce company majority-owned by Walmart, is integrating NeMo Guardrails, an open-source toolkit that enables developers to add programmable guardrails to LLMs, to enhance the safety of its conversational AI systems.
• Krutrim, part of the Ola Group of businesses that includes one of India’s top ride-booking platforms, is developing a multilingual Indic foundation model using Mistral NeMo 12B, a state-of-the-art LLM developed by Mistral AI and NVIDIA.
• Zoho Corporation, a global technology company based in Chennai, will use NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to optimize and deliver language models for its over 700,000 customers. The company will use NeMo running on NVIDIA Hopper GPUs to pretrain narrow, small, medium and large models from scratch for over 100 business applications.

India’s top global systems integrators are also offering NVIDIA NeMo-accelerated solutions to their customers.

• Infosys will work on specific tools and solutions using the NVIDIA AI stack. The company’s center of excellence is also developing AI-powered small language models that will be offered to customers as a service.
• Tata Consultancy Services has developed AI solutions based on NVIDIA NIM Agent Blueprints for the telecommunications, retail, manufacturing, automotive and financial services industries. TCS’ offerings include NeMo-powered, domain-specific language models that can be customized to address customer queries and answer company-specific questions for employees for all enterprise functions such as IT, HR or field operations.
• Wipro is using NVIDIA AI Enterprise software including NIM Agent Blueprints and NeMo to help businesses easily develop custom conversational AI solutions such as digital humans to support customer service interactions.

Wipro and TCS also use NeMo Curator’s synthetic data generation pipelines to generate data in languages other than English to customize LLMs for their clients.

To learn more about NVIDIA’s collaboration with businesses and developers in India, watch the replay of company founder and CEO Jensen Huang’s fireside chat at the NVIDIA AI Summit.

Read More