Google's TurboQuant Compresses LLM KV Cache Memory by 6x Without Accuracy Loss
By
HackMoN Ai
Summary
Google Research has introduced TurboQuant, a training-free compression algorithm presented at ICLR 2026 that dramatically reduces the memory footprint of Key-Value (KV) caches in large language models. The KV cache, which stores conversation history for models like ChatGPT, is a major cost driver — for a 70B model with 128K context, it consumes over 40GB of GPU VRAM. TurboQuant shrinks KV cache memory by 6x (from 16GB to under 3GB) with no measurable accuracy loss, potentially reducing server cluster requirements from 100 GPUs to just a few.
Source
bskyGoogle's TurboQuant Compresses LLM KV Cache Memory by 6x Without Accuracy Lossundercodetesting.comKey quotes
· 3 pulledEvery time ChatGPT replies, it remembers every word you've said. That memory — the Key-Value (KV) cache — is the real cost of running large language models, not the thinking itself.
For a 70B model serving 128K context, the KV cache alone consumes over 40GB of GPU VRAM, often exceeding the memory footprint of the model weights.
Google Research just shattered this bottleneck with TurboQuant, a training-free compression algorithm presented at ICLR 2026 that shrinks KV cache memory by 6x — from 16GB down to under 3GB — with zero measurable accuracy loss.
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