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LLM VRAM Needs: Real-world data reveals KV cache impact at 8K context

By PulseAugur Editorial · [1 sources] ·

A new analysis details the actual VRAM requirements for running 26 popular open-weight LLMs at 4-bit precision and an 8K context window. The study found that while weights often constitute the largest portion of memory usage, the KV cache size can become significant for models with long contexts, especially those with architectures that don't limit cache growth. The data, collected from GGUF file sizes and model configuration, is openly available for community use. AI

IMPACT Provides crucial data for local LLM deployment, helping users optimize hardware choices based on specific model architectures and context lengths.

RANK_REASON The item details empirical data collection and analysis of technical specifications for LLMs, akin to a research study. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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

LLM VRAM Needs: Real-world data reveals KV cache impact at 8K context

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The item details empirical data collection and analysis of technical specifications for LLMs, akin to a research study. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. dev.to — LLM tag TIER_1 English(EN) · Gary Nagra ·

    How much VRAM do popular LLMs really need at 4-bit and 8K context? Data from 26 models

    <p>"Will it fit on my card?" comes up constantly in local-LLM threads, and the usual answer is a rule of thumb like "about half a gigabyte per billion parameters at 4-bit." That rule is close enough for small dense models. For mixture-of-experts models, models with sliding-window…

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