TurboQuant dey cut LLM KV cache GPU memory 6x with no accuracy loss

Google Research tok say TurboQuant dey target one major bottleneck for LLM inference: di KV cache. Di company claim say e fit reduce GPU memory use during inference by at least 6× while e maintain “zero accuracy loss,” based on benchmark results. As context windows dey grow reach very large token counts, di KV cache fit expand to hundreds of GB per session. TurboQuant na compress di KV cache specifically (no be model weights). Google talk say dem avoid extra “quantization constants” using two methods: PolarQuant and QJL (Quantized Johnson-Lindenstrauss). For tests on open models like Gemma and Mistral, TurboQuant match full-precision performance under 4× compression and e preserve retrieval accuracy on “needle-in-haystack” tasks up to 104,000 tokens. Traders suppose note di scope: di “zero accuracy loss” claim dey apply to KV cache compression during inference, no be weights. Di approach still for lab stage and dem never validate am for large-scale production wey dey serve billions of requests. Full details dey planned for ICLR 2026, and early reports talk say e disturb some parts of di AI hardware supply chain. Crypto relevance likely indirect. More efficient inference fit eventually shift AI infrastructure cost expectations, but near-term moves for major crypto markets improbable without real deployments and external risk-flow catalysts.