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33 lines
1.4 KiB
Markdown
33 lines
1.4 KiB
Markdown
# TurboQuant
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KV cache compression for local inference on M4 Max MacBook Pro.
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## What
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TurboQuant (Google, ICLR 2026) is a three-stage KV cache compression method:
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1. **PolarQuant** — WHT rotation + polar coordinates + Lloyd-Max codebook (~4.2x compression)
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2. **QJL** — 1-bit quantized Johnson-Lindenstrauss residual correction
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3. **TurboQuant** — PolarQuant + QJL = ~3.5 bits/channel, zero accuracy loss
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## Why
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Unlock 64K-128K context on qwen3.5:27b within 32GB unified memory.
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A 27B model at 128K context with TurboQuant beats a 72B at Q2 with 8K context.
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## Status
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See [issues](https://forge.alexanderwhitestone.com/Timmy_Foundation/turboquant/issues) for current progress.
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## Roles
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- **Strago:** Build spec author
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- **Cid:** Implementation, benchmarks, deployment
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- **Locke:** Research support, upstream watch
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- **John:** Quality review
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- **Frankie:** Coordination
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## Source Repos
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- [TheTom/llama-cpp-turboquant](https://github.com/TheTom/llama-cpp-turboquant) — llama.cpp fork with Metal
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- [TheTom/turboquant_plus](https://github.com/TheTom/turboquant_plus) — Reference impl, 511+ tests
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- [amirzandieh/QJL](https://github.com/amirzandieh/QJL) — Author QJL code (CUDA)
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- [rachittshah/mlx-turboquant](https://github.com/rachittshah/mlx-turboquant) — MLX fallback
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## Docs
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- [Project Status](docs/PROJECT_STATUS.md) — Full project status and build specification
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