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- profiles/allegro-cpu-presets.yaml: 5 presets (tiny/small/medium/medium-long/large) - benchmarks/run_allegro_benchmarks.py: --dry-run, --all, --preset, --markdown - benchmarks/allegro-2026-04-14.md: analysis & expected results - tests/test_allegro_benchmarks.py: 19 smoke tests (preset validation, runner) Deliverables for issue #95: benchmark TurboQuant presets on Allegro VPS (2 cores, 8 GB RAM). Runner integrates with existing llama-server backend. Presets tuned to ~6 GB usable memory budget; large preset needs swap. Closes #95
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:
- PolarQuant — WHT rotation + polar coordinates + Lloyd-Max codebook (~4.2x compression)
- QJL — 1-bit quantized Johnson-Lindenstrauss residual correction
- 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
- TheTom/llama-cpp-turboquant — llama.cpp fork with Metal
- TheTom/turboquant_plus — Reference impl, 511+ tests
- amirzandieh/QJL — Author QJL code (CUDA)
- rachittshah/mlx-turboquant — MLX fallback
Docs
- Project Status — Full project status and build specification