Running 1M-token Context on a Single GPU (the Math)
This article explores the mathematical and technical challenges of running large language models with million-token context windows on a single GPU. It presents compression techniques and formulas to make this feasible.
Why it matters
Efficiently running large language models with long context windows is a critical challenge for advancing the state-of-the-art in natural language processing. This work provides a practical solution to this problem.
Key Points
- 1KV cache size is the main bottleneck, not model weights
- 217x compression allows a 70B model with 1M tokens to fit on 2 H100 GPUs
- 333x compression allows a 70B model with 1M tokens to fit on a single H100 GPU
- 4The author provides a Python formula to calculate the required KV cache size
Details
The article discusses the raw numbers behind running large language models with 1 million token context windows. It shows that for a 70B model, the KV cache alone would require 6TB of memory, far exceeding the capacity of current GPU hardware. However, the author presents compression techniques that can reduce this requirement by 17x or 33x, allowing the 70B model with 1M tokens to fit on 2 or even a single H100 GPU. The key is understanding the mathematical relationship between model size, context length, and KV cache size, which the author captures in a Python formula. This breakthrough enables running massive context windows on commodity GPU hardware, unlocking new capabilities for large language models.
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