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Comparing State-of-the-Art LLMs: The 8 Gigawatt Supercomputer Challenge

Comparing State-of-the-Art LLMs: The 8 Gigawatt Supercomputer Challenge
Photo by Solen Feyissa / Unsplash

Introduction

In the ever-evolving landscape of artificial intelligence, large language models (LLMs) continue to push the boundaries of what's possible. Recently, an intriguing question posed by Aravind Srinivas on Twitter sparked a fascinating exploration: How many H100 GPUs would be needed to build an 8 Gigawatt supercomputer cluster, and what would be the cost? We put this challenge to four leading LLMs - OpenAI's GPT-4o, Anthropic's Claude Sonnet 3.5, Meta's Llama 3.1, and Google's Gemini Advanced 1.5 - to see how they would approach this complex problem.

The Contenders' Responses

OpenAI GPT-4o

GPT-4o provided a detailed, step-by-step calculation:

  • Estimated 11,428,571 H100 GPUs needed
  • Total cost: Approximately $285.7 billion
  • Assumed 700 watts power consumption per GPU
  • GPU cost estimated at $25,000 each GPT-4 noted that this was a rough estimate and actual costs could vary significantly due to factors like cooling, infrastructure, and bulk discounts.

Claude Sonnet 3.5

Claude's approach was similar to GPT-4's:

  • Estimated 11,428,571 H100 GPUs needed
  • Total cost: Approximately $114.3 billion
  • Also assumed 700 watts power consumption per GPU
  • More conservative GPU cost estimate of $10,000 each Claude emphasized that this was a simplified calculation and highlighted additional factors like cooling systems, other hardware components, and infrastructure costs.

Meta Llama 3.1

Llama 3.1 took a slightly different approach:

  • Estimated 12,307,692 H100 GPUs needed
  • Total cost: Approximately $153.8 billion
  • Assumed 650 watts average power consumption per GPU
  • Estimated GPU cost between $10,000 - $15,000, using $12,500 for calculations
  • Considered cluster configuration (4 GPUs per node) and power supply efficiency Llama 3.1 provided a more detailed breakdown of the cluster structure and emphasized the theoretical nature of the calculation.

Google Gemini Advanced 1.5

Gemini took a unique approach, interpreting the question differently:

  • Assumed 8 Gigawatts referred to computing power (FLOPS) rather than electrical power
  • Estimated only 26 H100 GPUs needed
  • Total cost: Approximately $766,800
  • Based calculations on H100 SXM variant performance (313 TFLOPS)
  • Estimated GPU cost at $30,000 each Gemini's interpretation led to a significantly different result compared to the other models.

Analysis and Comparison

  1. Interpretation: Gemini's unique interpretation of the question as referring to computing power rather than electrical power led to a vastly different result. This highlights the importance of clear problem definition in AI interactions.
  2. Consistency: GPT-4o, Claude, and Llama 3.1 provided remarkably consistent estimates for the number of GPUs needed (11-12 million range) and overall cost ($114-285 billion range).
  3. Detail and Assumptions: Llama 3.1 provided the most detailed breakdown, considering factors like cluster configuration and power supply efficiency. GPT-4o and Claude offered similar levels of detail, while Gemini's different approach made direct comparison difficult.
  4. Cost Estimates: The models varied in their cost estimates for H100 GPUs, ranging from $10,000 (Claude) to $30,000 (Gemini). This variation significantly impacted the final cost projections.
  5. Additional Considerations: Claude and Llama 3.1 provided the most comprehensive list of additional factors to consider, such as cooling systems, infrastructure, and maintenance costs.
  6. Realism: All models, except Gemini, acknowledged the theoretical nature of the calculation and the unprecedented scale of such a supercomputer.

Conclusion

While all four models demonstrated impressive capabilities in tackling this complex problem, Llama 3.1 stands out for its comprehensive approach, detailed breakdown, and balanced consideration of various factors. GPT-4o and Claude performed similarly, providing consistent and well-reasoned estimates. Gemini's unique interpretation, while interesting, made it difficult to compare directly with the others.

This exercise showcases the strengths and limitations of current LLMs in handling complex, multifaceted problems. It highlights the importance of clear problem definition and the need for human oversight in interpreting AI-generated results, especially for unprecedented scenarios like this hypothetical supercomputer cluster.