What is KV cache quantization?

It compresses the keys/values stored during LLM inference , cutting memory use proportional to context length—Q8_0 halves FP16, Q4_0 quarters it.

Does KV cache quantization work on llama.cpp?

Yes—use `--cache-type-k q8_0 --cache-type-v q8_0` for 32K on 8GB VRAM with minimal quality loss.

Can I run 32K context on RTX 4060 with KV quantization?

Totally—Llama-3-8B Q4 fits at 6.4-7.4GB total, leaving headroom for RAG embeddings.

🤖 AI & Machine Learning

KV Cache Quantization: Squeezing 32K Context into 8GB VRAM Without Breaking a Sweat

Your RTX 4060 chokes on 32K context? KV cache quantization fixes that—halving or quartering memory use with barely a quality hit. Here's the how and why.

theAIcatchup Apr 08, 2026 3 min read

VRAM usage chart comparing FP16 vs Q8_0 and Q4_0 KV cache on RTX 4060 at 32K context

⚡ Key Takeaways

KV cache quantization slashes memory by 50-75% for long contexts, fitting 32K tokens in 8GB VRAM. 𝕏
llama.cpp makes it easy with --cache-type flags; Q8_0 for near-lossless, Q4_0 for max savings. 𝕏
Unlocks 'small model x long context' or 'large model x RAG'—reshaping local LLM tradeoffs. 𝕏

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theAIcatchup

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#KV cache quantization #LLM inference #VRAM optimization #llama.cpp

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Originally reported by Dev.to

⚡ Key Takeaways

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