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Q4 KV Cache Quantization: Cram 32K Contexts into 8GB VRAM — If the Math Holds

Your RTX 4060 chokes on 32K contexts because KV cache alone gulps 4GB. Q4 quantization fixes that — but only if you trust the math. Here's the cynical scoop.

theAIcatchup Apr 08, 2026 4 min read
Graph showing KV cache VRAM usage dropping from 4GB to 1GB at 32K context with Q4 quantization

⚡ Key Takeaways

  • KV cache, not weights, kills VRAM at long contexts — Q4 quant fixes it down to 1GB for 32K. 𝕏
  • Fits Llama-3-8B fully on 8GB cards like RTX 4060, enabling local long-context AI. 𝕏
  • Incremental win, but NVIDIA still profits as context lengths keep growing. 𝕏
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#KV cache quantization #LLM inference #Q4 quantization #VRAM optimization #llama-cpp #llama.cpp

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Originally reported by dev.to

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