Nvidia enterprise AI ecosystem: GPUs to dev tools

Nvidia is known for its H100 GPUs that run the world’s generative AI companies and turned Nvidia from a video game graphics giant into a multitrillion-dollar company. Billions of dollars worth of H100s power ChatGPT, Claude 3, Google Gemini and every major AI application on the planet. 

But Nvidia also has a whole ecosystem of enterprise AI tools from code frameworks and libraries to hosted cloud services. They have an end-to-end platform for enterprise AI, make it easy to deploy and scale enterprise AI applications, and give advanced developers the tools they need. 

Here’s a look at the whole Nvidia AI ecosystem – starting with their GPUs.

Nvidia GPUs & Systems

A100 tensor core GPUs: Still heavily used in AI data centers, the A100 GPU put Nvidia on the map as the go-to hardware for generative AI. The A100 80GB introduced the world’s fastest memory bandwidth at over 2 terabytes per second (TB/s) to run the largest language models and datasets.

H100 tensore core GPUs: This is the AI chip everyone needs in early 2024 – OpenAI, Meta, and Google have invested billions in these chips. While all the major AI players are developing their own chips, they each depend on hundreds of thousands of H100s to run their generative AI products.

Nvidia H100s cost $3.3K to build, are sold to customers for $30K and sell on marketplaces for up to $100K each. Meta has plans to use 350,000 NVIDIA H100 GPUs in their gen AI infrastructure by the end of 2024.

• Hopper architecture: Named after the pioneering computer scientist Grace Hopper, this architecture includes new technologies like the Transformer Engine, designed specifically to accelerate workloads such as large language models (LLMs).
• Increased efficiency: It offers improved energy efficiency, which is crucial for reducing operational costs in data centers.
• More memory: The H100 comes with more memory than its predecessor, the A100, crucial for handling large AI models and datasets.
• Multi-instance GPU (MIG) capability: This feature has been enhanced, allowing more flexibility in partitioning the GPU into smaller, isolated instances for different workloads.
• Scalability: It is designed to excel in large-scale AI projects, supporting complex, multi-GPU configurations that can tackle tasks previously deemed too challenging or time-consuming.
• Support for emerging AI technologies: The H100's architecture is optimized for the next generation of AI technologies, including generative AI models and more sophisticated deep learning algorithms.

DGX H100 Systems: The Nvidia DGX H100 combines multiple Nvidia H100 GPUs with optimized hardware and software components to deliver exceptional performance for AI training, inference, and scientific simulations. It’s a powerful, purpose-built AI system designed for large-scale AI workloads, high-performance computing, and data analytics. 

Here are some key features of the DGX H100 system:

• Up to 8 H100 GPUs: The DGX H100 can be configured with up to 8 Nvidia H100 GPUs, providing a massive amount of computational power for AI and HPC workloads.
• Software stack: DGX H100 comes with the Nvidia AI Enterprise software suite, which includes optimized versions of popular AI frameworks, libraries, and tools, such as TensorFlow, PyTorch, and Nvidia CUDA-X AI.
• Scalability: Multiple DGX H100 systems can be connected together to form even more powerful AI supercomputers, such as the Nvidia DGX SuperPOD, which can deliver exaflop-scale performance.
• CPU: The DGX H100 is equipped with Intel Xeon Scalable processors, providing additional computing power for CPU-based tasks and overall system management.
• HBM3 memory: The system features a large amount of high-bandwidth GPU memory (HBM3) and system memory to support memory-intensive workloads.
• Storage: DGX H100 includes high-speed NVMe SSDs for fast data storage and retrieval.
• Networking: The system supports high-speed networking interfaces, such as NVIDIA ConnectX-7 SmartNICs and InfiniBand, enabling fast data transfer and communication between multiple DGX systems.
• NVLink and NVSwitch: The system uses fourth-generation NVLink interconnects and Nvidia NVSwitch technology to enable high-speed communication between the GPUs, allowing them to work together efficiently on large-scale problems.

Blackwell B200 tensor core chip:  The B200 is the next generation of AI chip set to replace the Nvidia H100s in late 2024. They’ll reduce AI inference operating costs (e.g. running ChatGPT) and energy consumption by up to 25 times compared to the H100. 

• World's most powerful AI chip: The new GPUs under the Blackwell architecture contain 208 billion transistors. They utilize a 4NP TSMC process designed specifically for two-reticle limit GPU dies. These dies connect through a robust 10 TB/second chip-to-chip link, forming a unified GPU.
• Second-generation transformer engine: Blackwell leverages enhancements including micro-tensor scaling and NVIDIA’s dynamic range management algorithms. Integrated within the NVIDIA TensorRT™-LLM and NeMo Megatron frameworks, it offers twice the computing power and model capacity, enhanced by 4-bit floating point AI inference capabilities.
• Fifth-generation NVLink: The latest NVLink® iteration advances to a 1.8TB/s bidirectional throughput per GPU. This feature is designed to boost performance for AI models with multitrillion parameters and mixture-of-experts architectures. It supports high-speed communication among up to 576 GPUs, catering to the most complex LLMs.
• RAS engine: Blackwell GPUs incorporate a dedicated RAS (Reliability, Availability, and Serviceability) engine. This addition at the chip level employs AI for preventative maintenance, enabling diagnostics and reliability forecasting. It aims to maximize uptime and bolster resilience, essential for large-scale AI operations, potentially reducing operating costs by maintaining continuous operation.
• Secure AI: The architecture introduces advanced confidential computing to safeguard AI models and user data. It supports new encryption protocols for interfaces, crucial for industries requiring high privacy standards such as healthcare and financial services.
• Decompression engine: This engine supports the latest decompression formats, enhancing the performance of database queries. It plays a significant role in data analytics and science, sectors where GPU acceleration is expected to grow, reflecting the billions spent annually on data processing.

Read more about the Blackwell systems including the Blackwell GB200 Superchip and Blackwell DGX B200 System that will change data centers later this year.

NVIDIA NGC™ cloud services

NGC Enterprise Cloud: NVIDIA NGC™ is the portal of enterprise services, software, management tools, and support for end-to-end AI and digital twin workflows. 

NGC enables you to bring solutions to market faster with fully managed services, or take advantage of performance-optimized software to build and deploy solutions on your preferred cloud, on-prem, and edge systems.

NGC Use Cases

• Language modeling
• Recommender systems
• Image segmentation
• Translation
• Object detection
• ASR
• Text-to-speech
• HPC

NGC offers a collection of cloud services, including NVIDIA NeMo, BioNemo, and Riva Studio for generative AI, drug discovery, and speech AI solutions, and the NGC Private Registry for securely sharing proprietary AI software.

Together, these three stages enable RAG-enhanced LLMs to produce responses that are more accurate, detailed, and contextually aware than what a standalone generative model can achieve. 

Nvidia frameworks and libraries

CUDA toolkit: Nvidia's CUDA programming model is pivotal in unlocking the full potential of GPUs. It allows developers to use C++, Python, and other languages to accelerate compute-intensive applications by harnessing the parallel processing power of GPUs. 

CUDA has become a standard in the industry, supported by a wide range of software applications, libraries, and frameworks that facilitate AI development and deployment.

Nvidia NeMo framework: NVIDIA NeMo framework is a scalable and cloud-native generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech). 

The primary objective of NeMo is to provide a scalable framework for researchers and developers from industry and academia to more easily implement and design new generative AI models by being able to leverage existing code and pretrained models.

cuDNN and TensorRT: Nvidia's CUDA Deep Neural Network library (cuDNN) provides a GPU-accelerated library for deep neural networks, optimizing standard routines and enabling more efficient and faster training and inference. 

TensorRT complements this by optimizing deep learning models for production environments, ensuring low latency and high throughput for AI inference, critical for real-time applications.

Nvidia developer tools

Nvidia AI foundation models: Nvidia gives developers direct and easy access to the latest models – e.g. Llama 3, Mixtral 8x22B, Gemma, etc. 

Their APIs include AI models for vision tasks, drug discovery, biology, video games, and weather simulation.

Nvidia NIM: NIM bridges the gap between AI development and operational needs of enterprise environments. It offers optimized inference microservices for deploying AI models at scale – enabling 10-100x more enterprise application developers to contribute to AI transformation. 

Nvidia NIM also lets you develop with industry-standard APIs to simplify the development of AI applications. NIM APIs are compatible with standard development processes and let developers update AI applications quickly. This all leads to rapid deployment and scaling of AI enterprise solutions. 

Nvidia NeMo Platform: Different from the NeMo framework listed above, this is an end-to-end platform for developing custom generative AI anywhere.NeMo helps developers deliver enterprise-ready models with precise data curation, customization, RAG, and accelerated performance. 

It’s a complete solution for building enterprise-ready LLMs.

• Flexible
• Production ready
• Increases ROI
• Accelerated performance
• End-to-end pipeline

NGC development catalog: This catalog provides access to GPU-accelerated software that speeds up end-to-end workflows with performance-optimized containers, pretrained AI models, and industry-specific SDKs that can be deployed on premises, in the cloud, or at the edge.

Nvidia dev tools and ecosystem: This resource has everything from GPU-Accelerated Libraries to Cluster Management and Data Center Tools. If you’re a senior AI developer working with Nvidia, this’ll come in handy.

Nvidia LLMs

StarCoder 2: Nvidia released this family of open-source LLMs for code generation in collaboration with BigCode (backed by ServiceNow and HuggingFace.) StarCoder 2 supports hundreds of programming languages and delivers the best-in-class accuracy. It helps advanced developers build apps faster with code completion, auto-fill, advanced code summarization, and relevant code snippet retrievals.

The StarCoder2 family includes 3B, 7B, and 15B parameter models, giving flexibility to pick the one that fits your use case and meets your compute resources. StarCoder 2 has a context length of 16,000 tokens – letting it handle longer sections of code. The models have been trained responsibly, with 1 trillion tokens on permissively licensed data from GitHub. 

Nvidia ChatRTX: ChatRTX lets you personalize an LLM on your own content fast. It combines RAG, TensoRT-LLM, and RTX acceleration to query a custom chatbot quickly. The first of its kind released from a large company, it runs locally on your Windows RTX PC or workstation– making it secure and cost efficient.

Nvidia education

Deep Learning Institute (DLI): Nvidia provides teams with training and certifications in AI and deep learning. These educational resources are invaluable for teams looking to upskill or cross-train in AI technologies.

Benefits of Nvidia Deep Learning Institute: 

• Access to technical expertise
• Flexible training solutions
• Industry-standard tools and frameworks
• Applications across industries
• Earn certificates
• Real-world examples
• GPU-accelerated servers in the cloud
• Reduce time to production

Hire AI experts who know the Nvidia ecosystem 

To build enterprise-ready AI at scale you need AI software engineers that know the Nvidia ecosystem. Hiring internally could take 6-18 months but you need to start building AI solutions now, not next year. That’s why Codingscape exists. 

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