**Prepared by:** Timmy (execution) for Frankie's team (Strago, Cid, Locke, John)
**Spec:** turboquant-build-spec v2.2 (Strago)
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## Executive Summary
Phase 1 is COMPLETE. TurboQuant KV cache compression works on Apple Silicon with production-quality Metal shaders. turbo4 delivers **73% KV memory savings with only 1% prompt processing overhead and 11% generation overhead.** The path to 128K context on 36GB hardware is clear.
**Hardware correction:** The MacBook is M3 Max 36GB (not M4 Max 32GB as in spec). This INCREASES our memory budget from 27GB to ~31GB.
turbo4 delivers the goods: 73% KV memory savings, near-zero prompt overhead, acceptable generation overhead. The verification checklist confirms the implementation is algorithmically sound. The only gap is PPL testing, which is a corpus download away â not a fundamental risk.
TurboQuant works. PolarQuant KV cache compression delivers **73% memory savings with 1% prompt overhead**. 128K context on the MacBook becomes viable. Custom Ollama build is deferred (multi-day effort), but the fork's `llama-server` is a ready drop-in. Per-layer adaptive quantization is already implemented. QJL is infrastructure-only â not needed at current compression targets.
- Runtime optimizations: 4-mag LUT (M4+), sparse V dequant, profiling
**Note:** Allegro's analysis (checking only `master` branch) incorrectly concluded "NO TurboQuant." The implementation lives on the feature branch.
### PolarQuant Verification (#5): 5/6 PASS
| Item | Verdict |
|------|---------|
| WHT rotation (structured orthogonal) | PASS (Metal). CPU turbo4 ref uses dense random (legacy) |
| Same rotation quant/dequant | PASS |
| Lloyd-Max codebook (not uniform) | PASS |
| Radius at FP16+ | PASS |
| No per-vector normalization | PASS |
| Dequant matches quant in Metal | PASS |
**Flag:** CPU turbo4 reference path is algorithmically incompatible with Metal dequant. Only matters if CPU fallback invoked for turbo4. Metal production path is clean.
`TURBO4_USE_4BIT` defaults to 1 in `ggml-common.h`. The legacy 3-bit+QJL path exists but is disabled. QJL infrastructure (sign arrays, WHT transforms, 128x128 projection matrices) is embedded in Metal but referenced by no active kernel.
### Recommendation
**Not needed for current goals.** 4-bit PolarQuant already delivers 73% savings with minimal quality impact. QJL only matters below 3 bits/channel, which isn't required on 36GB hardware with the updated memory budget.
John wants maximum local inference quality on the MacBook Pro (M4 Max, 32GB unified memory) using TurboQuant-level KV cache compression. Currently running `qwen3.5:27b` via Ollama at `10.0.0.133:11434`. The goal: run a larger or better model within the same 32GB memory envelope by compressing the KV cache during inference.
TurboQuant (Google, ICLR 2026) is a three-stage KV cache compression method:
Community status: multiple `llama.cpp` forks, MLX proof-of-concepts, and a vLLM plugin exist. Nothing upstreamed to official `llama.cpp`, MLX, or Ollama yet. Author QJL code is public. Enough is public to build from.
This section specifies the PolarQuant algorithm concretely so Cid can verify that the community fork implements it correctly. A fork that gets the rotation wrong or uses the wrong codebook boundaries will compress successfully but degrade quality in ways that short PPL benchmarks may not catch â the damage surfaces during long production sessions with sustained context pressure.
- The paper uses a **Walsh-Hadamard transform** (WHT) â a structured orthogonal matrix that's O(d log d) to apply, not O(d²) like a dense random matrix.
- **Why:** Raw KV vectors have non-uniform coordinate distributions (some dimensions carry more energy). WHT spreads energy uniformly across all coordinates, making the post-rotation distribution predictable and concentrated. This is what eliminates the need for per-vector normalization constants.
- **Cid verification:** The fork must use a fixed WHT (or equivalent structured orthogonal rotation), not a learned or per-layer rotation. The rotation matrix must be identical at quantization and dequantization. If the fork uses a dense random matrix instead of WHT, it's functionally correct but slower â flag it.
- The angle coordinates are what get quantized. Because WHT made their distribution predictable, you can use a fixed codebook without per-vector calibration.
- Each angle coordinate is independently quantized using a **Lloyd-Max optimal scalar quantizer**.
- Lloyd-Max minimizes mean squared error for a known distribution. Because WHT makes the distribution analytically computable, the codebook boundaries are **precomputed once** and fixed for all vectors.
- **Codebook sizes by compression target:**
-`turbo4` = 4 bits per coordinate = 16 codebook entries per dimension
- **Cid verification:** Check that the fork's codebook boundaries match what the paper/PolarQuant paper specifies for the target distribution. If the fork uses uniform quantization instead of Lloyd-Max, that's a quality regression â uniform is simpler but wastes bits on low-probability regions.
**Critical property:** The inner product between a full-precision query Q and a PolarQuant-compressed K must be an unbiased estimator of the true Q·K dot product. The WHT rotation preserves this because orthogonal transforms preserve inner products. If the fork adds any non-orthogonal transformation (e.g., learned projection, PCA), the unbiasedness guarantee breaks.
1.**Walsh-Hadamard rotation matrix:** This is deterministic â a WHT of size d (model head dimension, typically 128) is computed from the recursive Hadamard construction. It's the same for every session, every model. Compute once at model load, store in memory. Cost: O(d log d) per head dimension â microseconds. No impact on model load time.
2.**Lloyd-Max codebook:** The quantization boundaries are precomputed for the known post-WHT distribution. For a given bit width (turbo4 = 4 bits = 16 entries), the codebook is a fixed lookup table of 16 boundary values + 16 reconstruction values. This is identical across sessions and models of the same head dimension. Can be hardcoded as a constant array or computed once at load time from the analytical distribution formula.
**Expected initialization overhead:** Negligible â both are small deterministic computations. But **measure it during Phase 1**: time the gap between Ollama receiving a request and the first token appearing, with and without TurboQuant. If initialization adds >1 second to cold model load, investigate caching the tables to disk alongside the model file.
**Use 27GB as the planning ceiling.** The v1 spec said "leaves 2GB for OS" at 30GB peak â that's too tight. All memory calculations below use 27GB available.
- This frees 4-15GB of headroom depending on context length
**Important:** These are calculated estimates, not measured values. Actual memory consumption can exceed theoretical due to fragmentation, allocation overhead, and implementation-specific buffering. Phase 1 **must** include actual peak memory measurement (see validation section). If measured exceeds calculated by >15%, the context ceiling drops accordingly.
### Model Recommendations
**Primary target: qwen3.5:27b at Q4_K_M with extended context**
- **Verdict: does not fit.** Even with TurboQuant, no room for KV cache + activations + Metal overhead. And quality at Q2_K is poor â weight quantization damage cancels the parameter count advantage.
**Recommended path: Stay on 27B class, use TurboQuant to unlock longer context (64K-128K) rather than a bigger model.** The real win on 32GB unified is context length, not parameter count. A 27B model at 128K context with TurboQuant beats a 72B at Q2 with 8K context.
**Alternative worth testing: Mistral/Codestral 25B-class models** at Q5_K_M (~18GB) with TurboQuant. Locke's research notes TurboQuant was benchmarked on Mistral â community results may be more reproducible.
| **`TheTom/llama-cpp-turboquant`** | `llama.cpp` fork with Metal support | Most directly useful â same stack as Ollama. Reports PPL numbers on M-series. | Community fork, not upstream. May lag `llama.cpp` HEAD. |
| **`amirzandieh/QJL`** | Author's QJL CUDA implementation | Official author code. CUDA kernels, eval scripts, LongBench commands. | CUDA only â needs Metal port for MacBook. Phase 2 dependency. |
The v1 spec understated this. Community `llama.cpp` forks can diverge significantly from HEAD, especially in the Metal backend where Apple Silicon optimizations change frequently. The risk isn't "it doesn't build" â it's "it builds fine on the fork's base commit but breaks when cherry-picked onto current HEAD."
- **KV cache layer:** `llama.cpp` has refactored KV cache internals multiple times in 2026. A fork based on a 4-week-old commit may touch structs/functions that have been renamed or restructured upstream.
- **Metal shaders:** Apple Silicon Metal optimizations are actively changing. Custom Metal kernels for TurboQuant dequant may conflict with upstream shader refactors.
- **Memory management:** `ggml` memory allocation has evolved. The fork's cache allocation assumptions may not match current `ggml` memory pools.
1.**Check fork freshness:**`git log --oneline -1` on the fork. Compare base commit date against `llama.cpp` HEAD. If >4 weeks stale, flag as HIGH risk.
3.**If stale (2-4 weeks):** Attempt cherry-pick of TurboQuant-specific commits onto current HEAD. If merge conflicts are limited to TurboQuant files â resolve manually. If conflicts touch core KV cache / Metal code â stop, evaluate effort.
4.**If very stale (> 4 weeks) or conflicts are extensive:** Switch to **clean-room approach** â use `TheTom/turboquant_plus` as the algorithm reference and implement the KV cache types directly into current `llama.cpp` HEAD. This is more work (~60-90 min instead of ~20-40 min) but avoids the merge conflict maze.
5.**Escape hatch:** If `llama.cpp` path is blocked, fall back to `rachittshah/mlx-turboquant` (MLX native, no fork divergence risk, but requires API proxy for Ollama compatibility).
**Cid decision point:** After Step 0, report fork age + conflict assessment before proceeding. If clean-room is needed, update the time estimate and Frankie adjusts the schedule. Don't spend more than 15 minutes fighting merge conflicts â switch to clean-room.
The spec assumes the `llama.cpp` fork has working **Metal shaders** for PolarQuant KV dequantization. KV dequant happens in the attention computation hot path â every token, every layer, every head. If the fork only has CPU or CUDA dequant kernels and no Metal implementation, the MacBook will either:
- Fall back to CPU dequant â **catastrophic** performance loss (10-50Ã slower attention)
**If Metal shaders exist:** Proceed with `llama.cpp` fork path (primary).
**If Metal shaders do NOT exist:** MLX becomes the **primary** path, not the fallback. Switch to `rachittshah/mlx-turboquant` immediately. Reframe Phase 1 around MLX + API proxy for Ollama compatibility. Report this finding before spending any more time on the `llama.cpp` path.
This check takes 2 minutes and determines the entire build strategy. Do it first.
**Fallback: MLX standalone** if `llama.cpp` Metal integration proves too complex. `rachittshah/mlx-turboquant` as starting point. Would require a small proxy server to maintain API compatibility with our Ollama endpoint.
| 1 | Long-context summarization | Feed 20K tokens of a research paper, ask for structured summary with citations | KV cache quality at length â compressed K/V must retain source detail |
| 3 | Code generation | Write a Python script with 3 functions, error handling, type hints | Precise token prediction â code is unforgiving of subtle quality drops |
| 4 | Code debugging | Provide buggy code (3 bugs), ask to identify and fix all three | Attention to detail across context â must reference earlier code correctly |
| 5 | Factual recall (early context) | Provide 10 facts in the first 1K tokens, continue for 8K tokens of filler, ask about fact #3 | Retrieval from early context through compressed KV |
| 6 | Creative writing | Write a 500-word short story with specific constraints (setting, character, twist) | Compression artifacts surface as repetition or coherence loss |
| 7 | Multi-turn conversation | 10-turn technical Q&A where later questions reference earlier answers | Cross-turn coherence through accumulated compressed KV |
| 8 | Structured output | Generate a JSON schema with 15+ fields, nested objects, and validation rules | Format precision â compressed KV must maintain structural consistency |
| 9 | Translation + analysis | Translate a paragraph ENâES, then analyze the translation choices | Tests both generation quality and meta-reasoning about own output |
| 10 | Instruction following | Complex prompt with 8 specific formatting requirements (headers, bullet style, word limits, etc.) | Whether compression causes the model to "forget" constraints mid-generation |
**Prompts must be written and saved to `projects/sovereign-stack/turboquant-test-prompts.md` before Phase 2 benchmarks run.** Same prompts, same order, both configurations. This prevents unconscious cherry-picking.
**Asymmetric K/V test:** Run K at Q8_0, V at turbo4. Community reports this works well on sensitive models. Compare PPL vs symmetric turbo4 K+V.
**Long-session quality test (Phase 2 only):** Short-context PPL benchmarks can miss quality degradation that surfaces during sustained context pressure. During Phase 2, run one extended production simulation:
- **Measure actual peak resident memory** at each context length (`footprint -p <pid>` or `vmmap --summary`). Compare measured vs calculated. If measured exceeds calculated by >15%, note the delta â it reduces the achievable context ceiling.
- Report: PPL delta per context length, tok/s delta, **measured peak memory per context length**, max context before OOM/swap, asymmetric vs symmetric results
**Deliverable:** Working `llama.cpp` build on MacBook with PolarQuant KV cache. PPL + performance numbers. Section 1a verification checklist completed.
**2-hour build troubleshooting cap:** If the `llama.cpp` fork doesn't compile and pass a basic smoke test (load model, generate 10 tokens) within 2 hours of troubleshooting, stop. Pivot to MLX path. Don't sink more time into Xcode/CMake/Metal debug loops when a working MLX PoC exists. Report what broke â the information is useful even if the path is abandoned.
**Decision gate:** If PPL delta ⤠0.5 and tok/s ⥠90% baseline AND Section 1a checklist passes â proceed to Phase 2. If PPL fails but checklist passes â try asymmetric K/V or lower compression (turbo3 instead of turbo4). If checklist fails â fix implementation before trusting benchmarks.
Ollama pins a specific `llama.cpp` commit and calls it through CGo bindings in `llm/`. If our fork changes any function signatures, struct layouts, or enum values that Ollama's Go code references, the build will either fail or produce subtle runtime bugs.
```bash
# Clone Ollama source
git clone https://github.com/ollama/ollama.git
cd ollama
# Find the pinned llama.cpp commit
cat llm/llama.cpp/CMakeLists.txt | head -5 # or check go.mod / Makefile
# Diff our fork's API surface against Ollama's expected API
# Focus on: llama.h, ggml.h function signatures that Ollama calls
If API surface differs: check if TurboQuant changes are additive (new functions/types only) or modify existing signatures. Additive = safe. Modified existing = need to update Ollama's CGo bindings.
**Build steps:**
- Build custom Ollama binary using our `llama.cpp` fork as submodule
- Deploy to MacBook as replacement Ollama
- Verify existing endpoint (`10.0.0.133:11434`) works identically
- Run full test matrix (all 4 quality tests + all 4 performance tests)
- Test with qwen3.5:27b at 64K and 128K context
- If 128K works: update Ollama model config to advertise larger context
- Run 10-prompt practical generation comparison for John review
**Deliverable:** Production Ollama on MacBook with TurboQuant KV cache. Full benchmark report. John signs off on quality.
**Estimated Cid time:** 15-25 min (Ollama build is straightforward once `llama.cpp` fork is validated).
Not all transformer layers have equal sensitivity to KV cache quantization. Research and community experimentation show early layers (first 2-4) and late layers (last 2-4) tend to be more sensitive than middle layers. If the fork supports per-layer KV cache type configuration:
- **Sensitive layers (first 3 + last 3):** K at Q8_0, V at turbo4 (or full FP16 KV)
- **Middle layers:** K and V both at turbo4 (or even turbo3)
This gives the same average compression ratio as uniform turbo4 but concentrates precision where it matters most. The PPL improvement can be meaningful (0.1-0.3) at zero memory cost.
**When to pursue:** Only after Phase 2 is stable and baseline quality is confirmed. This is tuning, not architecture. If uniform turbo4 passes all quality gates, per-layer optimization is nice-to-have, not necessary.
**Cid note:** During Phase 1, check whether the fork exposes per-layer KV type config. If it does, note it for later. Don't implement it yet.
- **Weight quantization** â TurboQuant is KV cache compression only. Model weight quantization (GGUF Q4_K_M etc.) is a separate concern and already handled by Ollama.
- **Predator (desktop) deployment** â this spec targets MacBook only. Predator runs NVIDIA (CUDA) which is a different kernel backend. Can extend later.
- **Multi-model serving** â TurboQuant helps with single-model memory but doesn't change Ollama's single-model-at-a-time constraint.
- **Ollama upstream contribution** â out of scope for now. We build for ourselves first.
- **MacBook Pro memory:** Confirmed M4 Max 32GB from memory/2026-03-14.md. If it's actually 36GB or 48GB (M4 Max comes in 36/48/128 configs), that changes the model ceiling. Can Cid check `sysctl hw.memsize` on the MacBook during Phase 1? Non-blocking â doesn't change the approach, just the model size ceiling.
