Why You Will Marry the Wrong Person: The Intersection of Relationships and GPU Demand

Why You Will Marry the Wrong Person: The Intersection of Relationships and GPU Demand

In a wiser, more self-aware society, we would ask potential partners, "And how are you crazy?" This unconventional question, posed in an article titled "Why You Will Marry the Wrong Person," challenges the notion of normalcy in relationships. Similarly, in the world of technology, the demand for high-end GPUs like the Nvidia H100 raises questions about what companies truly need and why they choose certain products over others.

The article on relationships emphasizes that we often appear normal to those who don't know us well. This notion can be applied to the technology industry, where companies have unique requirements and preferences that may not be immediately apparent. Understanding these individual needs is crucial to making informed choices.

When it comes to the demand for GPUs, it is clear that startups play a significant role. Many new companies are building models from scratch, fine-tuning existing models, and securing multi-million dollar contracts. For these startups, speed and efficiency are paramount, making the H100 the preferred choice for both training and inference. Its impressive performance, memory bandwidth, and cache latencies make it the go-to option for many LLM companies.

However, the question arises: why aren't LLM companies using AMD GPUs? While theoretically possible, the time and effort required to make them work may outweigh the benefits. The development time alone could be a hindrance, potentially causing these companies to fall behind their competitors. Nvidia's CUDA technology, therefore, acts as a barrier to entry, solidifying their position in the market.

Considering the demand for GPUs, it's essential to understand the specific requirements of different companies. GPT-4, for example, was likely trained on thousands of A100s, while startups like Meta, Inflection, and Anthropic may require tens of thousands of H100s. The sheer magnitude of these numbers, coupled with the cost per GPU, highlights the substantial investment companies are willing to make.

But who manufactures these GPUs, and how long does it take to produce them? TSMC is responsible for producing the H100s, and the entire production, packaging, and testing process takes approximately six months. Understanding these timelines is crucial for companies planning their GPU acquisitions and deployments.

Additionally, the allocation of GPUs plays a significant role in meeting demand. Large clouds like Azure, Oracle, and AWS have all launched their H100 previews, with Nvidia providing allocations to customers based on various factors. Nvidia's preference for customers with strong brand names or startups with impressive pedigrees demonstrates their strategic approach to allocation.

Now that we have explored the intersection of relationships and GPU demand, it is essential to extract actionable advice from these insights:

1. Understand your unique requirements: Just as individuals have their own quirks and idiosyncrasies, companies have specific needs when it comes to technology. Take the time to evaluate your requirements and choose the GPU that best aligns with your goals.

2. Consider the long-term implications: While it may be tempting to opt for the latest and most powerful GPUs, consider the potential challenges that could arise. Compatibility issues and development time can significantly impact your time to market, so weigh the benefits against the risks before making a decision.

3. Build strategic partnerships: Nvidia's preference for certain customers highlights the importance of strategic partnerships. Consider aligning your company with strong brand names or leveraging your startup's pedigree to secure better allocations and support from GPU manufacturers.

In conclusion, the parallels between relationships and GPU demand are more apparent than one might expect. Just as we must understand our partners' quirks and idiosyncrasies, companies must comprehend their unique requirements when choosing high-end GPUs. By evaluating these needs, considering the long-term implications, and building strategic partnerships, companies can make informed decisions that benefit their bottom line.