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LLM KV Cache Memory Usage Explained: MQA and GQA Techniques

By PulseAugur Editorial · [1 sources] ·

The KV cache, essential for fast LLM generation, consumes significant GPU memory by storing keys and values for each token. Techniques like Multi-Query Attention (MQA) and Grouped-Query Attention (GQA) reduce this memory footprint by sharing key-value heads across multiple query heads. GQA, used in models like Llama-3 8B, offers a compromise between memory efficiency and model quality, making longer context windows more feasible for serving and self-hosting. AI

IMPACT Understanding KV cache and attention mechanisms like GQA is crucial for optimizing LLM deployment and managing GPU resources.

RANK_REASON The item explains technical concepts related to LLM inference efficiency.

Read on dev.to — LLM tag →

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

LLM KV Cache Memory Usage Explained: MQA and GQA Techniques

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

    The KV cache is eating your GPU. MQA and GQA are how models fight back

    <p>Quick recap from the last post: the KV cache saves you from recomputing old tokens, and it's the reason generation is fast.</p> <p>But it has a bill. And the bill is memory.</p> <p>I wanted to put an actual number on it, because "the cache is big" doesn't mean much until you s…

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