Calculating the KV Cache Memory Usage of Large Language Models
This article provides a formula and examples to calculate the exact KV cache memory usage of popular large language models like Llama and Mistral. It also discusses the impact of compression techniques on reducing the memory footprint.
Why it matters
Accurately estimating the memory usage of large language models is crucial for their efficient deployment and scaling, especially as model sizes continue to grow.
Key Points
- 1The formula to calculate KV cache memory usage is: 2 x L x H x d x T x 2, where L is the number of transformer layers, H is the number of attention heads, d is the head dimension, and T is the sequence length.
- 2The article includes memory usage tables for Llama-3-8B, Mistral-7B, Llama-3-70B, and Mixtral-8x7B models at different context lengths.
- 3The article shows that with NexusQuant compression, the KV cache of a 70B model can be reduced from 160 GB to 16 GB, allowing it to fit on a single A100 GPU.
Details
The article focuses on the KV cache memory usage of large language models, which is a critical factor in their deployment and performance. It provides the exact formula to calculate the KV cache size, which takes into account the number of transformer layers, attention heads, head dimension, and sequence length. This allows engineers to precisely determine the memory requirements of different models and configurations. The article then presents detailed examples for popular models like Llama and Mistral, showcasing their KV cache usage at various context lengths. Finally, it discusses the impact of NexusQuant compression, which can reduce the KV cache size by up to 33x with minimal impact on perplexity. This enables even the largest 70B models to fit on a single high-end GPU, significantly simplifying their deployment and reducing hardware requirements.
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