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TurboQuant

KV cache compression for local inference on M4 Max MacBook Pro.

What

TurboQuant (Google, ICLR 2026) is a three-stage KV cache compression method:

  1. PolarQuant — WHT rotation + polar coordinates + Lloyd-Max codebook (~4.2x compression)
  2. QJL — 1-bit quantized Johnson-Lindenstrauss residual correction
  3. TurboQuant — PolarQuant + QJL = ~3.5 bits/channel, zero accuracy loss

Why

Unlock 64K-128K context on qwen3.5:27b within 32GB unified memory. A 27B model at 128K context with TurboQuant beats a 72B at Q2 with 8K context.

Status

See issues for current progress.

Roles

  • Strago: Build spec author
  • Cid: Implementation, benchmarks, deployment
  • Locke: Research support, upstream watch
  • John: Quality review
  • Frankie: Coordination

Source Repos

Docs

Description
TurboQuant KV cache compression for local inference — PolarQuant + QJL on M4 Max via llama.cpp/Ollama. Build spec from Strago, build by Cid, coordination by Frankie.
Readme MIT 1.3 MiB
Languages
Python 90.5%
C++ 6.2%
Metal 2.4%
CMake 0.9%