AI article
40 GB to 6.7 GB: The 3-Bit Trick That Shrinks Your KV Cache 6x
Community description: Your LLM doesn't reread the conversation every time it generates a word. It keeps notes — a key and a...
Dev.to | Oct 22, 2026 | Daniel Sam Pete Thiyagu
Automated excerpt
A 70-billion-parameter model at 128K context burns 40 GB of GPU memory on the KV cache alone — nearly double the headroom left on two H100s after the weights load. Llama 3. 1 70B, KV cache only: 32K: 10 GB → 1. 7 GB. Weight quantization (AWQ/GPTQ, 4x) and KV quantization (TurboQuant, 6x) stack: an AWQ 4-bit 70B (~35 GB weights) plus a TurboQuant 128K cache (6. 7 GB) fits ~42 GB on a single 80 GB H100 — a workload that previously needed multiple GPUs.
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