Why H200s Will Have a Huge Impact on AI Development: Comparing H200 to H100

You can’t talk about AI model training and fail to mention graphics processing units (GPUs). GPUs are specialized processing cores originally designed to process visual data and images but have now become important in model training and inference. NVIDIA GPUs, particularly H100 and H200s, are some of the most used in the market. 

Both H100 and H200 GPUs are based on NVIDIA Hopper™ architecture. H100s have been the front runner in the market for premier computing power in 2024. However, the new H200 boasts enhancements that deliver superior computing power. This article will examine the critical differences between the H100 and H200 GPUs, and explore why the H200 is poised to propel AI innovation to unprecedented heights.  TH200 vs. H100: A Comparison of Key Features

Tensor Core Advancements

Both the H100 and H200 feature advanced tensor cores optimized for AI workloads. NVIDIA H200 Tensor Core GPU comes with enhanced features like 141 GB of HBM3e with 4.8 TB/s of memory bandwidth. 

The H200 tensor cores are ideal for generative AI applications and large language models (LLMs) as they are known for reduced latency and improved parallelism. H200 tensor cores leverage enhanced support for FP8, FP16, and INT8, optimized for next-generation AI models. H200 has better results in mixed-precision operations as it can effortlessly switch between lower precisions (like FP8) and higher precisions (FP16) while maintaining accuracy. 

Memory Bandwidth Comparison

The H100s have high memory bandwidth, the HBM2e offering 3.35 TB/s. However, this has been further improved with the H200’s HBM3, offering 4.8 TB/s. This means that the H200 offers approximately 1.4 times faster data access and reduced training times. H200 also has higher peak throughput and more memory channels, allowing larger datasets to be processed in parallel and faster. 

Memory bandwidth is important in training complex models and large datasets. High bandwidth ensures smooth data flow between GPU memory and cores, preventing delay when training models with billions or parameters. 

Energy Efficiency and Performance Gains

Energy consumption is one factor to consider when choosing a GPU. An NVIDIA H100 GPU can consume up to 700 watts (W) of power. H200, on the other hand, consumes 50% less than the H100 for key LLM inference workloads. The improvement in H200 offers improved performance while maintaining lower energy consumption.

Reduced power consumption has long-term operational costs for companies scaling AI projects. Companies scaling projects across cloud environments and cloud centers will not only save on power bills but also on cooling requirements, thus achieving more sustainable operations. This also translates to lower total cost of ownership (TCO), meaning more cost-effective scaling of AI projects over time. 

Multi-GPU Scalability: NVLink Enhancements

Both H100 and H200 support multi-GPU configurations via NVLink 4.0. However, the H200 has enhanced NVLink capabilities, allowing for even faster inter-GPU communication. The enhancements in H200 support better data transfer between GPUs and synchronization, which translates to more efficient scaling across larger clusters. 

H200s also have architectural optimizations that reduce communication bottlenecks, translating to smoother performance for inference workloads and distributed AI training. The ease of deploying multi-node GPU systems makes H200 ideal for high-performance AI systems needed by LLMs and advanced simulations. 

Performance Impact: How the H200 Outshines the H100 in AI Development

AI Model Training: Speed and Efficiency Gains

H200’s next-gen architecture, HBM3e memory with 141 GB of capacity and 4.8 TB/s bandwidth, leads to faster training times compared to the H100’s 80 GB and 3.35 TB/s. This reduces training time for common AI models like GPT, BERT, and vision AI models by reducing bottlenecks in data transfers and memory handling.

In offline scenarios, the H200 achieves upto 31,712 tokens per second, while H100 GPUs get up to 22,290 tokens per second, representing a 42.4% increase. 

Real-Time AI Inference

Real-time inference is a crucial feature in real-time AI applications such as autonomous driving, AI-based decision systems, and real-time recommendation engines. H100 has 8GB memory capacity while H200 has 141GB, allowing the latter to manage more complex models and reduce the need for external data transfers. 

According to NVIDIA, H200 nearly doubles the inference speed on Llama 2, a 70 billion-parameter LLM, compared to the H100. H200 is also expected to improve its performance with software updates. 

Generative AI: Better Performance with H200

There is a growing demand for generative AI models such as LLMs, as well as image and video generation models. Even though H100 GPUs can comfortably handle these tasks, the enhancements in H200 lead to better speed and accuracy. H200 GPUs produce up to 31,000 tokens/second, a record on MLPerf’s Llama 2 benchmark. NVIDIA also TensorRT software on H200 GPUs to maximise the efficiency of the new hardware. 

Aethir's Solution: Cost-Effective Access to H100s and H200s

• Globally Distributed GPU Network

Aethir presents a globally distributed GPU network that provides access to H100 and H200 GPUs at competitive prices. These enterprise-grade GPU resources are aggregated from data centers, gaming studios, and crypto-mining companies. 

Aethir solves supply chain challenges by offering immediate availability of these high-demand GPUs. The company maintains a robust network of nodes through its partnerships with reputable hardware providers. 

• Cost-Effective Rental Model

100 GPU chips, priced around $10,000, are more affordable than the H200, yet still a significant investment for many AI startups and individuals. Despite the high cost, both H100 and H200 GPUs are top-tier options for AI model training.

Aethir offers companies, particularly startups and scaling businesses, access to cutting-edge AI technology without requiring hefty upfront hardware investments. By utilizing Aethir's distributed network, companies can reduce expenses related to traditional providers by up to 80%. This approach eliminates the need to construct and maintain costly data centers.

• Flexibility and Scalability for Startups and Growing Companies

AI workloads can fluctuate, meaning there are instances where businesses need a lot of computing power and, in other cases, less power. Purchasing GPUs to match peak demand may result in situations where such businesses have idle GPUs during low-demand seasons. Businesses may also face supply chain delays when they want to buy GPUs to match demand. 

Aethir’s rental model is ideal for startups and companies scaling their AI operations. It offers flexible rental terms that can adapt to project demands. As such, businesses can add GPUs by renting during peak seasons or dropping some during low demand. Such an approach ensures that they only spend on what they need. 

Use Cases: How Companies Are Leveraging H200 and H100 for AI Development

Enterprise AI at Scale

Enterprises are leveraging both H100 and H200 GPUs to power large-scale AI models, from autonomous systems to real-time analytics. In an Instagram post, Mark Zuckerberg, Meta CEO, said that the company will acquire 350,000 H100 GPUs from chip designer Nvidia by the end of 2024. These chips will be used to train its next-gen model, Llama 3. The new model is expected to have better math-solving, reasoning, and coding capabilities. 

H100 and H200 chips are applicable in many enterprise applications, such as autonomous driving, healthcare, and financial services. For instance, the enhancements in H200 reduce inference times, making them ideal for autonomous vehicles, specifically in path planning and real-time object detection. 

AI Startups and Innovators

Innovators and AI startups face resource constraints, making it hard for them to compete with large enterprises. Aethir’s decentralized infrastructure gives AI startups and innovators cost-effective access to H100 and H200 GPUs. This means that such players don’t have to incur the high initial cost associated with acquiring GPU hardware or the cost of running it. 

TensorOpera, an AI startup, leveraged Aethir’s platform to train a 750-million-meter model in 30 days. Digi Tech, a subsidiary of DIGIASIA Corp, has already acquired 5,120 NVIDIA H200 GPUs to develop, train, and deploy advanced AI solutions for the government, telecom, and fintech sectors. 

Wrapping up

The NVIDIA H200 Tensor Core GPU is ideal for high-performance computing (HPC)and generative AI workloads. It is the first GPU with 141 gigabytes (GB) of HBM3e memory at 4.8 terabytes per second (TB/s). H200 has nearly double the capacity of H200 GPUs and 1.4X more memory bandwidth. H200’s larger and faster memory accelerates LLMs and generative AI systems with lower total costs of ownership and better energy efficiency. 

While H200s are effective in training large and complex models, they are low in suppply and cost prohibitive for many businesses to acquire. Aethir’s globally distributed GPU network offers a critical solution by renting on-demand high-quality H100 and H200 chips to enterprises and individuals. 

Startups and enterprises alike can ,can take advantage of leveraging  Aethir’s rental model to gain access to the latest AI technology. This option allows businesses to  drive innovation without the upfront costs of hardware ownership or concerns about hardware obsolescence. This model is a cost effective solution that allows businesses  to scale according to needs. For more information about Aethir's AI GPU offerings, request more information here or check aethir.com/ai.