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Local LLM context size dramatically increases RAM requirements

By PulseAugur Editorial · [1 sources] ·

Running large language models locally requires significant random-access memory, particularly as context window sizes increase. For smaller models with around 8 billion parameters, a 4K context uses approximately 0.5 GB of RAM, scaling up to 16 GB for a 128K context. Medium-sized models, ranging from 14 to 32 billion parameters, also show a substantial increase in RAM usage with larger contexts, with an 8K context consuming about 1.5 GB. AI

IMPACT Local LLM deployment requires careful resource management, with context window size being a key factor in RAM consumption.

RANK_REASON The item discusses practical considerations for running LLMs locally, focusing on resource requirements rather than a specific release or research breakthrough.

Read on Mastodon — fosstodon.org →

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

Local LLM context size dramatically increases RAM requirements

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The item discusses practical considerations for running LLMs locally, focusing on resource requirements rather than a specific release or research breakthrough.
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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    Just something to keep in mind when you're tuning your local LLM setup. The size of your context can really eat up your RAM! Small Models (~8B Parameters) Conte

    Just something to keep in mind when you're tuning your local LLM setup. The size of your context can really eat up your RAM! Small Models (~8B Parameters) Context size KV Ram used 4K (Default) ~0.5 GB 8K ~1.0 GB 32K ~4.0 GB 64K ~8.0 GB 128K ~16.0 GB Medium Models (~14B to 32B Par…

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