"Nvidia H100 GPUs: Supply and Demand" - The Consumer's Hierarchy of Preferences: Two Ends of the Strategy Spectrum

"Nvidia H100 GPUs: Supply and Demand" - The Consumer's Hierarchy of Preferences: Two Ends of the Strategy Spectrum

In the world of technology, the demand for high-end GPUs is constantly growing. One such GPU that has gained significant attention is the Nvidia H100. This article explores the supply and demand dynamics surrounding the H100 GPUs and delves into the different strategies employed by retailers to cater to consumer preferences.

Startups doing significant fine-tuning large open source models are the primary users of the H100 GPUs. These startups, often working on multi-million dollar contracts, require hundreds to thousands of GPUs to build new models from scratch. For companies using private clouds, the usage is largely focused on training large language models (LLMs) and some diffusion model work. The H100 GPUs are the preferred choice for these tasks due to their speed and scalability.

When it comes to LLM training and inference, companies tend to prefer the H100 GPUs for training and prioritize performance per dollar for inference. The H100 GPUs offer faster training times and better scalability, making them ideal for startups looking to compress time to launch or train their models. Factors such as memory bandwidth, FLOPS, caches, cache latencies, and compute performance play a crucial role in LLM training, making the H100 GPUs more favorable than the A100s.

While theoretically companies can opt for AMD GPUs, the time it takes to get everything to work poses a significant challenge. The development time required may result in being later to market than competitors, making Nvidia's CUDA the preferred choice for many. Additionally, the risk of deploying a large number of AMD GPUs or startup silicon chips is a significant investment that companies may not be willing to take.

The cost of the H100 GPUs varies depending on the configuration. For example, a DGX H100 with 8 H100 GPUs costs around $460,000, including the required support. Startups can avail the Inception discount, reducing the cost by $50,000. The number of GPUs needed for different projects varies, with GPT-4 likely trained on thousands of A100s and companies like Meta, Tesla, and Stability AI using thousands of A100s.

The sheer number of H100 GPUs that companies might want is staggering. OpenAI, Inflection, Meta, and various cloud providers may require tens of thousands of H100s, amounting to billions of dollars' worth of GPUs. This demand also excludes Chinese companies like ByteDance, Baidu, and Tencent, who are likely to require a substantial number of H800s.

The production of H100 GPUs takes around six months, considering production, packaging, and testing. TSMC is the manufacturer of these GPUs, with CoWoS (3D stacking) packaging being the bottleneck in the production process.

Retailers employ two different strategies to cater to consumer preferences. The first strategy focuses on building a brand and charging a premium. This is achieved by creating a positive association with the product through advertising. The second strategy focuses on increasing volumes and reducing costs to pass on the savings to consumers. Retailers adopting this strategy position their brand in terms of consumer value and emphasize low prices.

Creating loyal customers with a unique value proposition is crucial for retailers. Consistency in operations and branding is essential to avoid brand dilution. Companies that deviate from their established brand image may struggle to regain their luxury status, as seen in the case of Coach. Leaving some consumer surplus allows retailers to maintain a competitive position and build long-term customer loyalty.

In conclusion, the demand for Nvidia H100 GPUs is driven by startups and companies working on fine-tuning large open source models. The preference for H100 GPUs over AMD GPUs is primarily due to the time required to get AMD GPUs working effectively. The cost and number of GPUs needed for various projects are significant factors in the supply and demand dynamics. Additionally, retailers employ different strategies to cater to consumer preferences, focusing on brand building or cost efficiency. Consistency in operations and branding is crucial to avoid brand dilution and create long-term customer loyalty.