Calculating GPU Memory Savings with NexusQuant Compression
This article provides a detailed analysis of how much GPU memory can be saved using NexusQuant, a compression technique for transformer models' key-value caches. It includes a formula to calculate the KV cache size and a Python calculator to estimate the savings on different GPU hardware.
Why it matters
Optimizing GPU memory usage is crucial for deploying large language models in production, especially for applications that require long input sequences. The insights and tools provided in this article can help AI researchers and engineers make more efficient use of their GPU resources.
Key Points
- 1The KV cache size formula: 2 × num_layers × num_heads × head_dim × seq_len × bytes_per_element
- 2NexusQuant can provide 10x, 17x, or 33x compression, significantly increasing the maximum token sequence length that can fit on a GPU
- 3Practical scenarios show how NexusQuant enables serving larger context on A10G, maximizing throughput on A100 80GB, and enabling long-context research
Details
The article delves into the technical details of transformer models' key-value (KV) caches and how much GPU memory they consume. It provides the formula to calculate the KV cache size, which is a function of the number of layers, heads, head dimension, and sequence length. For a 7B model like Mistral-7B, the KV cache can take up to 16.7 GB of GPU memory at 128K tokens. The article then showcases the memory savings enabled by NexusQuant, a compression technique that can provide 10x, 17x, or 33x reduction in KV cache size. This allows serving much larger context on the same GPU hardware, from 128K tokens on an A10G to 17M tokens on an A100 80GB with the 33x compression preset. The article also includes a Python calculator to estimate the KV cache size and savings for different model configurations and GPU specifications.
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