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Llama 3.1 8B KV Cache Demands 16 GiB for 128K Tokens

By PulseAugur Editorial · [1 sources] ·

A technical blog post details the significant memory requirements of the Llama 3.1 8B model, particularly concerning its KV cache. The author explains that a single 128K token sequence can consume approximately 16 GiB of memory solely for the KV cache, in addition to the model's ~16 GB weights. This calculation, based on the formula 2 × layers × KV heads × head_dim × bytes per value, highlights why a 24 GB GPU may not be sufficient for such long contexts. The post suggests solutions like reducing the maximum sequence length, using FP8 for the KV cache, quantizing weights, or upgrading to a larger GPU. AI

IMPACT Highlights memory constraints for self-hosting large context models, impacting deployment strategies and hardware choices.

RANK_REASON Technical blog post explaining memory usage for a specific model and tool.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Llama 3.1 8B KV Cache Demands 16 GiB for 128K Tokens

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Technical blog post explaining memory usage for a specific model and tool.
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · jidonglab ·

    KV Cache Math: Llama 3.1 8B Needs 16 GiB Just to Hold 128K Tokens

    <p>The model fit. That was the part that confused me.</p> <p>Llama 3.1 8B in bf16 is about 16 GB of weights. My card had 24 GB. I set <code>max_model_len=131072</code> because the model card says 128K context, hit run, and vLLM refused to start. Something about the max sequence l…

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