RotorQuant vs TurboQuant: 31x Speed Claim - Reality Check (Local AI) скачать в хорошем качестве

RotorQuant vs TurboQuant: 31x Speed Claim - Reality Check (Local AI)

With Google’s TurboQuant you can get 5x KV cache compression and fit more LLM context into same hardware. But the speed hit for prompt processing is very real. In this video, I break down how RotorQuant (IsoQuant / PlanarQuant) tries to tackle the problem at its core - but do they succeed? Do the claims of 9–31× speed improvement hold up? You’ll learn: How KV Cache actually stores data How TurboQuant solves Q4 compression accuracy issue Why solving accuracy adds compute cost and latency How RotorQuant / IsoQuant optimize this compute cost How 3D Game Engine math finds its way to AI cache quantization How missing Apple Metal kernel implementation appears in logs as high graph splits count Previous video (TurboQuant benchmark):    • TurboQuant Isn’t the Local AI Revolution (...   RotorQuant implementation tested: https://www.scrya.com/rotorquant/ TurboQuant implementation tested: https://github.com/TheTom/turboquant_... https://github.com/TheTom/llama-cpp-t... Models tested: Qwen3.5 35B A3B Q4_K_M Qwen3.5 27B Q4_K_S ⏱️ Chapters 00:00 - TurboQuant vs RotorQuant 00:51 - RotorQuant Claims 01:35 - The Plan 01:52 - KV Cache Mechanics & Q4 Quantization Challenge 04:05 - TurboQuant Solution & Accuracy 05:35 - TurboQuant Compute Cost Issue 06:26 - RotorQuant / IsoQuant Solution to Compute Issue 08:27 - RotorQuant / IsoQuant llama.cpp Build & Test 10:20 - “graph splits” and Missing Metal kernels 11:24 - Outro