TurboQuant-MLX

Extreme weight and KV cache compression for LLMs on Apple Silicon. MLX implementation of Google's TurboQuant (Zandieh et al., 2025) — Hadamard rotation + Lloyd-Max codebooks applied both to weights (compile time) and the KV cache (run time).

Supports dense models (LLaMA, Qwen, Mistral), Mixture-of-Experts (Qwen-MoE, GPT-OSS, Qwen3.5-MoE, Qwen3.6-35B-A3B, Qwen3-235B-A22B, DeepSeek-V2/V3), and Mamba/attention hybrids (Nemotron-3-Nano-4B, Nemotron-3-Super-120B). Compatible with hybrid attention architectures, attention sinks, sliding-window attention, and linear attention layers.

With both weight and KV cache compression at 3-bit, GPT-OSS-120B fits its full 131K context window in 50 GB on a 64 GB MacBook — and KV cache compression actually makes generation faster on the 120B (8.7 vs 6.4 tok/s) because the smaller cache cuts memory bandwidth more than dequant costs.

Expert streaming (v0.4.0) runs MoE models whose weights exceed available RAM by paging only the router-selected experts from disk per token — e.g. the 35B-parameter Qwen3.6-35B-A3B runs on a 16 GB Mac mini in under 4 GB of RAM, with output bit-identical to the fully-resident model. See Qwen3.6-35B-A3B on a 16 GB Mac mini.

Local coding — Qwen3.6-27B, a dense SWE-bench-grade coder, runs fully resident on a 48 GB Mac at 3-bit (~13 GB on disk, ~17.5 GB at runtime) and serves to Cursor / VS Code over an OpenAI-compatible endpoint. See Qwen3.6-27B.

Key Results — Weight Compression

Model	Method	Bits	PPL	Size	Gen Speed (M4 Max)
Qwen2.5-7B	TurboQuant	3	8.92	3.5 GB	—
Qwen2.5-7B	Affine	3	13.37	3.3 GB	—
GPT-OSS-20B	Affine (mlx-lm)	4	—	11.2 GB	148 tok/s
GPT-OSS-20B	MXFP4 (original)	4	83.04	12.8 GB	—
GPT-OSS-20B	TurboQuant	4	72.63	11.2 GB	—
GPT-OSS-20B	TurboQuant	3	78.60	9.3 GB	73 tok/s
GPT-OSS-120B	Affine 4-bit (mlx-community)	4	—	65.8 GB	Doesn't fit 64GB
GPT-OSS-120B	MXFP4 (original)	4	—	63.5 GB	Doesn't fit 64GB
GPT-OSS-120B	TurboQuant	3	—	48 GB	44 tok/s
GPT-OSS-120B (hybrid for 48GB)	TQ 3-attn / 2-experts, gs=32	2/3 mix	—	~35 GB	42–50 tok/s
GPT-OSS-120B	TurboQuant	2	—	32 GB	51 tok/s (poor quality)
Qwen3.5-122B-A10B	BF16 (original)	16	—	~240 GB	Doesn't fit 64GB
Qwen3.5-122B-A10B	TurboQuant	3	—	~50 GB	26.5 tok/s (64 GB) · streams on a 16 GB Mac mini
Qwen3.6-35B-A3B	TurboQuant, gs=32	3	—	~16 GB	~60 tok/s (resident) · runs in <4 GB via streaming
Qwen3.6-27B (dense coder)	TurboQuant, gs=32	3	—	~13 GB	~14 tok/s (resident) · fits 48 GB, SWE-bench coder
Qwen3-235B-A22B-Instruct-2507 (hybrid)	TQ 3-attn / 2-experts, gs=32	2/3 mix	—	70.5 GB	~4–6 tok/s (64 GB, 40 GB cache) · converts + streams on a 16 GB Mac mini
Qwen3-235B-A22B-Instruct-2507 (full 3-bit)	TurboQuant, gs=32	3	—	103 GB	~1.3 tok/s (64 GB, 40 GB cache) · recall-critical sibling, passes 6/6 stress
Nemotron-3-Nano-4B	TurboQuant	3	—	~2.2 GB	75.6 tok/s
Nemotron-3-Super-120B-A12B	BF16 (original)	16	—	~240 GB	Doesn't fit 64GB
Nemotron-3-Super-120B-A12B	TurboQuant	3	—	~50 GB	18.7 tok/s
Nemotron-3-Super-120B-A12B (hybrid for 48GB)	TQ 3-attn / 2-experts, gs=32	2/3 mix	—	~36 GB	~27.2 tok/s

Key Results — KV Cache Compression

Model	KV cache config	KV size	Speed	Notes
GPT-OSS-20B (FP16 weights)	FP16 KV	27.0 MB	90.6 tok/s	baseline
GPT-OSS-20B (FP16 weights)	TQ 3-bit KV	7.79 MB	29.9 tok/s	3.5x cache savings
GPT-OSS-120B (TQ 3-bit weights)	FP16 KV	45.0 MB	6.4 tok/s	baseline
GPT-OSS-120B (TQ 3-bit weights)	TQ 3-bit KV	11.83 MB	8.7 tok/s	*3.8x cache savings — and faster* than FP16**
GPT-OSS-120B (TQ 3-bit weights)	TQ 4-bit KV	12.21 MB	16.0 tok/s	also clean
Qwen3.5-122B (TQ 3-bit weights)	FP16 KV	161.06 MB	5.4 tok/s	baseline
Qwen3.5-122B (TQ 3-bit weights)	TQ 3-bit KV	150.17 MB	5.7 tok/s	output identical to FP16

KV cache compression projects to ~7 GB RAM saved at 131K context on GPT-OSS-120B and ~5 GB at 262K on Qwen3.5-122B. Roundtrip cosine similarity vs FP16: 0.983 at 3-bit, 0.995 at 4-bit.

On the Qwen3.5-122B KV rows: these were measured with symmetric 3-bit KV (demo_kv.py --tq-bits 3, short prompt), and the rates sit well below the model's fully-resident decode. With the Metal wired cap raised so the ~50 GB model stays fully resident, decode with the recommended mixed K8/V3 cache is ~24–25 t/s — and there fp16 edges out compression at short-to-moderate context, the gap widening as context grows. On this model KV compression is a memory win, not a speed win; the genuine decode speed-up is GPT-OSS-120B-specific. See the resident long-context sweep in #19.

Key Results — Apple M5 Pro (48 GB, Metal4)

First Metal4 / MTLGPUFamilyApple10 data point, contributed by @sbayer2 (#14, #16) — a new 48 GB tier between the 16 GB Mac mini and the 64 GB M4 Max. Reproduce with benchmarks/bench_m5_pro.py.

Model	Mode	Gen t/s	Peak Memory	Notes
Qwen3.6-35B-A3B tq3-g32	resident	52.0	18.1 GB	fp16 KV
Nemotron-3-Super-120B-A12B tq3a/tq2e-g32	resident	20.2	41.1 GB	needs `sudo sysctl iogpu.wired_limit_mb=49152`
Qwen3.5-122B-A10B tq3	streaming (30 GB cache)	9.4 (7.3 e2e)	34.2 GB	89.9% expert hit-rate

KV cache sweep on the 35B (resident, 3-run averages):

KV config	Prompt t/s	Gen t/s	Peak
fp16 baseline	47.8	52.0	18.131 GB
K8 / V3	75.5	45.7	18.123 GB
K8 / V3 + sink128	76.1	45.7	18.124 GB
K3 / V3	75.6	45.2	18.122 GB

122B expert streaming — parallel prefetch (--cache-budget-gb 30, 256 tokens):

`--prefetch-workers`	Gen t/s	E2E t/s	Disk read	Hit rate
1 (serial)	7.4	5.7	44.3 GB	89.5%
8 (parallel)	9.1	7.6	41.9 GB	90.1%
Speedup	1.23×	1.33×

A 1.23× decode speedup from --prefetch-workers 8, landing between the Mac mini (1.3×) and the M4 Max (1.67×). The M5 Pro MacBook Pro SSD is the limiter — parallel prefetch helps but doesn't saturate the way the M4 Max's higher-bandwidth SSD does.

122B expert streaming — cache-budget sweep (--prefetch-workers 8, 256 tokens):

Budget	Hit rate	Gen t/s	E2E t/s	Peak Metal	Disk read
20 GB	80.9%	7.1	6.1	24.2 GB	80.7 GB
30 GB	90.3%	9.1	7.6	34.2 GB	40.9 GB
38 GB	91.0%	8.9	7.5	42.2 GB	38.0 GB

The hit-rate curve flattens hard past 30 GB (only +0.7% for +8 GB), and throughput actually dips at 38 GB as peak Metal (42.2 GB) crowds the wired cap. 30 GB is the sweet spot on the 48 GB tier — 90%+ hit rate with ~14 GB of headroom for OS stability; pushing to 38 GB gives negligible gain while peaking uncomfortably close to the wired limit.

KV compression gives a consistent ~1.6× prompt-processing speedup for a ~12% decode cost (long-context decode behavior is in The speed flip). Expert-streaming hit-rate scales with the cache budget — 44.6% at 4 GB (16 GB mini) → 89.9% at 30 GB (48 GB), a ~7× throughput jump that fills the gap between the 16 GB and 64 GB tiers.

Stability near the memory ceiling: long-context prompt prefill close to the wired cap can starve the kernel watchdog (a watchdogd / AppleARMWatchdogTimer panic). A rapidly-growing KV cache makes Metal commit pages continuously (AGXG17XFamilyResidencySet _commitAddedAllocations), so the binding limit is allocation rate, not peak — a static 41 GB resident model (Nemotron) is stable, while a growing ~30 GB KV during a 63K-token prefill can panic. Practical limits on the 48 GB tier (#14): contexts up to ~14.5K are safe on the 35B with either KV config; 63K is feasible with K8/V3 (28.7 GB peak) but not fp16 (30.8 GB → panic). Keep headroom and close other apps for long-context runs near the cap.

Install

pip install turboquant-mlx-full

The package is published as turboquant-mlx-full on PyPI, but importable as turboquant_mlx (without the -full suffix) — this matches the original project name and the examples in the Medium articles.

import turboquant_mlx
from turboquant_mlx.layers import TurboQuantKVCache, convert_cache_to_turboquant

Requirements

macOS with Apple Silicon (M1/M2/M3/M4)
Python 3.10+
64 GB unified memory recommended for 20B+ models

The Metal kernels are JIT-compiled by MLX at first use, so no Xcode / CMake toolchain is required to install the package.

Install from source (for development)

git clone https://github.com/manjunathshiva/turboquant-mlx.git
cd turboquant-mlx
pip install -e .

For evaluation utilities (perplexity benchmarking), also install the optional dependencies:

pip install "turboquant-mlx-full[eval]"

Quick Start

1. Convert a model to TurboQuant format

# Dense model (e.g., LLaMA 3.2 1B at 3-bit)
python -m turboquant_mlx.convert \
    --hf-path meta-llama/Llama-3.2-1B \
    --mlx-path ./llama-3.2-1b-tq3 \
    --bits 3 --group-size 64

# MoE model (e.g., GPT-OSS-20B at 2-bit)
python -m turboquant_mlx.convert \
    --hf-path openai/gpt-oss-20b \
    --mlx-path ./gpt-oss-20b-tq2 \
    --bits 2 --group-size 64

# Very large model whose quantized form won't fit in RAM (200B+): --streaming
# writes each layer to a shard and frees it, so peak memory stays ~one shard
# (5 GB) + one layer — letting 235B/671B-class MoEs convert on a 64 GB Mac.
python -m turboquant_mlx.convert \
    --hf-path Qwen/Qwen3-235B-A22B-Instruct-2507 \
    --mlx-path ./qwen3-235b-tq3 \
    --bits 3 --group-size 64 --streaming

2. Generate text

turboquant-generate \\
    --model ./gpt-oss-20b-tq2 \
    --prompt "Why is the sky blue? Explain in simple terms." \
    --max-tokens 200

3. Evaluate perplexity

python -m turboquant_mlx.evaluate \
    --hf-path openai/gpt-oss-20b \
    --bits 2 3 4 \
    --num-samples 256 --seq-len 512

4. Generate with KV cache compression

The production turboquant-generate CLI accepts KV-cache flags directly (v0.2+). Use mixed K/V precision (--kv-k-bits 8 --kv-v-bits 3) — required for TurboQuant-quantized weights, and lossless on stock fp16 weights:

# v0.2 recommended default: mixed K8/V3 + 128-token fp16 sink
turboquant-generate \
    --model ./gpt-oss-120b-tq3 \
    --prompt "Why is the sky blue?" \
    --max-tokens 1024 --temp 0.7 \
    --kv-k-bits 8 --kv-v-bits 3 --kv-min-tokens 128

# Symmetric (legacy) — only safe on fp16 weights
turboquant-generate \
    --model openai/gpt-oss-20b \
    --prompt "Why is the sky blue?" \
    --max-tokens 200 --kv-bits 3

# Side-by-side comparison harness (4 configs in one run)
python -m turboquant_mlx.benchmarks.demo_kv_v02 \
    --model ./gpt-oss-120b-tq3 \
    --prompt "Why is the sky blue?" \
    --max-tokens 1024 --temp 0.7 --top-p 0.9 --repetition-penalty 1.1

5. Serve a TurboQuant model over an OpenAI-compatible API

turboquant-serve wraps mlx_lm.server and patches its loader so any TurboQuant model (quantization.mode = "turboquant" in config.json) loads through the PolarQuant path. Non-TurboQuant models pass through unchanged, so this is a drop-in replacement for mlx_lm.server.

# Serve a local TQ model
turboquant-serve \
    --model ./NVIDIA-Nemotron-3-Super-120B-A12B-BF16-tq3 \
    --port 8080

# Or serve directly from the Hugging Face Hub
turboquant-serve \
    --model manjunathshiva/Nemotron-3-Super-120B-A12B-tq3 \
    --port 8080

Then call it like any OpenAI-compatible endpoint. The model field in the request must match the string passed to --model:

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "./NVIDIA-Nemotron-3-Super-120B-A12B-BF16-tq3",
    "messages": [{"role": "user", "content": "Why is the sky blue?"}],
    "max_tokens": 4096,
    "temperature": 0.7
  }'

For Nemotron-3 reasoning models, prefer max_tokens >= 2048 so the <think> trace and the final answer both fit. mlx-lm splits them into message.reasoning (the thinking) and message.content (the answer).

From Python via the OpenAI SDK:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8080/v1", api_key="not-needed")
resp = client.chat.completions.create(
    model="./NVIDIA-Nemotron-3-Super-120B-A12B-BF16-tq3",
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
    max_tokens=4096,
    temperature=0.7,
    stream=True,
)
for chunk in resp:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

All mlx_lm.server flags forward unchanged — see turboquant-serve --help for --host, --temp, --top-p, --prompt-cache-size, etc.

Serve a model bigger than RAM (expert streaming)

Passing --cache-budget-gb routes the loader through the streaming path, so a MoE whose weights exceed RAM can be served over the OpenAI API — only the router-selected experts are paged from disk per token. This is how you put a ~50 GB 122B on a 16 GB Mac mini behind Claude Code / Aider:

turboquant-serve \
    --model manjunathshiva/qwen3.5-122b-tq3 \
    --cache-budget-gb 4 \
    --kv-k-bits 8 --kv-v-bits 3 --kv-min-tokens 128 \
    --prompt-concurrency 1 --port 8080

The Flash-MoE streaming levers ride along: --max-active-experts (K-reduction, default 4 → ~2× less disk I/O) and --use-page-cache / --no-page-cache (auto by model-size-vs-RAM — trust-OS is ~2.4× faster decode when the model fits free RAM, F_NOCACHE otherwise). Pair with --kv-* to compress the growing KV cache of long agentic loops, and --prompt-concurrency 1 since streaming is a single-user path.

Note: mlx_lm.server is intended for development and local use, not production. It does not implement authentication or rate limiting.

Memory tuning when serving near the unified-memory ceiling

Serving a 50 GB model on a 64 GB Mac (or any TQ model that fills most of RAM on a 48 GB / 96 GB Mac) leaves very little headroom for Metal command buffers and accumulating prompt caches. After 3-4 multi-turn requests the server can crash with:

libc++abi: terminating due to uncaught exception of type std::runtime_error:
[METAL] Command buffer execution failed: Insufficient Memory
(00000008:kIOGPUCommandBufferCallbackErrorOutOfMemory)

mlx_lm.server keeps a persistent prompt cache per role/conversation to speed up follow-up turns. Each new prompt grows that pool, and once caches + model weights + decode workspace exceed Metal's wired-memory budget, the next allocation aborts the process.

Two fixes, in order of impact:

1. Raise Metal's wired-memory ceiling (biggest lever, requires sudo, resets on reboot):

# 64 GB Mac → leave ~7 GB for macOS
sudo sysctl iogpu.wired_limit_mb=57344

# 48 GB Mac → leave ~5 GB for macOS
sudo sysctl iogpu.wired_limit_mb=43008

To make it permanent, append iogpu.wired_limit_mb=57344 to /etc/sysctl.conf.

2. Cap the prompt cache (works without sudo, evicts oldest cached prompts to stay under the cap):

turboquant-serve \
    --model ./NVIDIA-Nemotron-3-Super-120B-A12B-BF16-tq3 \
    --port 8080 \
    --prompt-cache-bytes 2147483648    # 2 GB hard cap

Tighter caps (536870912 = 512 MB, or --prompt-cache-size 1 to keep only one sequence) trade follow-up prefix-cache speedup for stability.

Recommended combo for Nemotron-3-Super-120B-A12B-tq3 on a 64 GB Mac:

sudo sysctl iogpu.wired_limit_mb=57344
turboquant-serve \
    --model ./NVIDIA-Nemotron-3-Super-120B-A12B-BF16-tq3 \
    --port 8080 \
    --prompt-cache-bytes 2147483648

Also close any other GPU users (Chrome/Electron apps, Final Cut, Xcode simulators) before launching — even an idle Chrome can be holding 1-2 GB of unified memory.

Compress the KV cache while serving

--prompt-cache-bytes caps how much the reuse pool across requests can grow, but each in-flight request still holds an fp16 KV cache that scales with context length. On a memory-constrained box — e.g. a streaming 120B on 16 GB driven by an agentic loop (Aider, Claude Code) whose prompts grow every turn — that per-request cache is usually what runs you out of memory first. turboquant-serve adds the same KV-quant flags as turboquant-generate to shrink it ~4x in place:

turboquant-serve \
    --model manjunathshiva/Nemotron-3-Super-120B-A12B-tq3 \
    --port 8080 \
    --kv-k-bits 8 --kv-v-bits 3 \   # mixed-precision KV (recommended default)
    --kv-min-tokens 128 \           # keep first 128 tokens fp16 (attention sinks)
    --prompt-concurrency 1

Flag	Meaning
`--kv-k-bits N` / `--kv-v-bits M`	Mixed-precision KV; K8/V3 is the recommended default
`--kv-bits N`	Symmetric K=V=N (legacy; not recommended below 3)
`--kv-min-tokens N`	Keep first N cached tokens in fp16 (sink protection)
`--kv-group-size G`	Hadamard rotation group size (default 64)

These are processed by turboquant-serve and stripped before the remaining flags forward to mlx_lm.server. See KV Cache Compression below for how to choose bit-widths and the speed/quality trade-offs.

Single-stream when KV-quant is on. TurboQuant KV caches don't support the cross-request merge that mlx_lm.server's batch generator needs, so enabling any --kv-* flag makes the server serve requests sequentially. That's the right trade-off for a single-user setup; for a multi-client server it means concurrent requests queue rather than batch.

KV Cache Compression

TurboQuant KV cache compression applies the same Hadamard rotation + Lloyd-Max codebook pipeline to KV vectors at runtime. The compressed cache is dequantized to float16 only when attention needs it, so it routes through MLX's standard scaled_dot_product_attention and is compatible with attention sinks, sliding windows, and linear attention layers.

Programmatic usage

from turboquant_mlx.layers import convert_cache_to_turboquant
from mlx_lm.models.cache import make_prompt_cache

# 1. Build per-layer cache (correct types for hybrid models)
cache = make_prompt_cache(model)

# 2. Convert to TurboQuant KV cache (v0.2 mixed K/V + sink protection)
cache = convert_cache_to_turboquant(
    cache,
    k_bits=8, v_bits=3,           # K-precision-critical, V tolerates 3-bit
    min_tokens_before_quant=128,  # keep first 128 tokens fp16 (attention sinks)
    group_size=64,
)

# 3. Process the prompt and generate — cache is compressed from token 128+
model(prompt_tokens, cache=cache)
for token in generate_loop(model, cache):
    ...

v0.1 → v0.2 migration: tq_bits=3 still works (symmetric K=V=3) but is not recommended on TurboQuant-quantized weights. Pass k_bits=8, v_bits=3 instead. Pre-existing checkpoints and code paths are fully backward compatible.

Choosing a bit-width (v0.2)

K precision matters far more than V precision: softmax amplifies any K error, while V tolerates aggressive quantization. Mixed K8/V3 is the new default.

Weights	K bits	V bits	sink	When to use
FP16 / BF16	8	3	128	Default — lossless quality, ~4× smaller cache
FP16 / BF16	4	3	128	More aggressive; small quality dip on dense attention
TurboQuant-quantized	8	3	128	Required on tq3 weights — symmetric K3 collapses past ~1k generated tokens
Any	8	4	128	Highest fidelity TQ KV setting

Why K8 specifically on TurboQuant weights: stacking 3-bit K cache on top of already-3-bit weight quantization compounds the noise enough to break long-form generation on GPT-OSS-20B (we observed total output collapse past ~800 tokens with K3_V3 on tq3 weights, while K8_V3 is clean). The same K3_V3 cache is fine on stock fp16 weights — the failure mode is co-compression, not the cache alone.

CLI flags

turboquant-generate exposes the same controls:

turboquant-generate --model ./model-tq3 --prompt "..." \
    --kv-k-bits 8 --kv-v-bits 3 \
    --kv-min-tokens 128 \
    --kv-group-size 64

Flag	Purpose
`--kv-bits N`	Symmetric K=V=N (legacy v0.1)
`--kv-k-bits` / `--kv-v-bits`	Mixed precision (v0.2 recommended)
`--kv-min-tokens N`	Keep the first N cached tokens in fp16 (sink protection)
`--kv-group-size N`	Hadamard rotation group size (default 64)

The speed flip

Whether KV compression speeds up or slows down decode depends on the per-token KV cache size, not the parameter count. When the per-token KV is large (many KV heads and/or long context), its 4x smaller footprint cuts memory bandwidth more than dequant adds, and decode is faster than FP16. When it is small (few active params, short context), dequant overhead dominates and compression is slower — a pure memory optimization.

Model	FP16 KV	TQ 3-bit KV	Direction
GPT-OSS-20B	90.6 tok/s	29.9 tok/s	TQ is 3x slower
Qwen3.6-35B-A3B (3B active)	52.0 tok/s	45.7 tok/s	TQ is 1.1x slower
GPT-OSS-120B	6.4 tok/s	8.7 tok/s	TQ is 1.4x faster

The penalty also grows with context on small-KV models. A community long-context benchmark on M5 Pro (#14) measured Qwen3.6-35B-A3B-tq3-g32 decode at four context lengths (256 gen tokens, except 63K which used 128):

Context	KV Config	Prompt t/s	Decode t/s	Peak Metal	Memory Saved
~65 tok	fp16	47.8	52.0	18.13 GB	—
~65 tok	K8/V3	76.1	45.7	18.12 GB	0.01 GB
~2.5K tok	fp16	131.9	51.5	20.50 GB	—
~2.5K tok	K8/V3	133.4	38.2	20.55 GB	-0.05 GB
~14.5K tok	fp16	132.8	46.0	22.50 GB	—
~14.5K tok	K8/V3	132.6	16.7	21.94 GB	0.56 GB
~63K tok	fp16	124.5	34.5	30.79 GB	—
~63K tok	K8/V3 (no sink)	123.6	5.2	28.71 GB	2.08 GB

The fp16 decode advantage widens with context — 1.14× at 65 tokens → 1.35× at 2.5K → 2.75× at 14.5K → 6.6× at 63K. So on small-active MoEs, use KV compression to fit longer contexts in less RAM (2.08 GB saved at 63K) — not to speed them up. The flip to faster shows up only on GPT-OSS-120B (8.7 vs 6.4 t/s). The similarly-sized Qwen3.5-122B does not flip — run resident on a 64 GB M4 Max, fp16 KV beats mixed K8/V3 at every context (1.07× at 256 → 1.20× at 4096; #19) — so the speed-up isn't a general 120B-class property; it's specific to GPT-OSS-120B's KV geometry.

Compatibility

Feature	Supported	Notes
Attention sinks	Yes	GPT-OSS sink vectors flow through standard SDPA
Sliding window attention	Yes	`RotatingKVCache` layers are left untouched
Linear attention	Yes	`ArraysCache` (Qwen3.5 GatedDeltaNet) is left untouched
Hybrid architectures	Yes	Per-layer cache type is preserved
Prompt-first conversion	Yes	Process prompt with FP16, convert before generation

Running GPT-OSS MoE Models on Apple Silicon

GPT-OSS-20B (21B total, 32 experts, 3.6B active)

Hardware: Apple M4 Max 64GB (or any Apple Silicon with 16GB+ unified memory at 3-bit)

Step 1: Convert to TurboQuant 3-bit (recommended)

python -m turboquant_mlx.convert \
    --hf-path openai/gpt-oss-20b \
    --mlx-path ./gpt-oss-20b-tq3 \
    --bits 3 --group-size 32

Model size: 9.3 GB (vs 12.8 GB MXFP4 original — 28% smaller, lower perplexity)

The converter automatically:

Detects MoE architecture (SwitchLinear / QuantizedSwitchLinear layers)
Dequantizes MXFP4 expert weights to float
Applies Hadamard rotation + Lloyd-Max codebook quantization
Keeps router weights and attention at full precision
Handles blockwise Hadamard for 2880-dim experts (2880 = 9 x 320)

Step 2: Generate text

turboquant-generate \\
    --model ./gpt-oss-20b-tq3 \
    --prompt "Explain quantum entanglement to a 10-year-old." \
    --max-tokens 256

Expected: ~73 tok/s generation, ~85 tok/s prefill on M4 Max

Step 3: Run a quick quality check

python -m turboquant_mlx.evaluate \
    --hf-path openai/gpt-oss-20b \
    --bits 3 \
    --no-affine --no-qjl \
    --num-samples 64 --seq-len 512

All bit-widths for GPT-OSS-20B

Method	Bits	Size	Peak RAM	Gen Speed	Quality
Affine (mlx-lm)	4	11.2 GB	~14 GB	148 tok/s	Coherent (but see note below)
TurboQuant	4	11.2 GB	~14 GB	—	Best (PPL 72.63, beats MXFP4)
TurboQuant	3	9.3 GB	~12 GB	73 tok/s	Recommended (PPL 78.60, beats MXFP4, coherent)
TurboQuant	2	7.5 GB	~10 GB	—	Poor (incoherent generation on pre-quantized models)

Speed vs quality tradeoff: Affine 4-bit is ~2x faster on the 20B model due to simpler dequantization, but TurboQuant 3-bit is 28% smaller with lower perplexity than both affine 4-bit and OpenAI's own MXFP4. Crucially, affine 4-bit cannot scale to 120B on 64GB hardware — TurboQuant 3-bit is the only option there.

# 4-bit (best quality, beats OpenAI's MXFP4)
python -m turboquant_mlx.convert \
    --hf-path openai/gpt-oss-20b \
    --mlx-path ./gpt-oss-20b-tq4 \
    --bits 4 --group-size 32

GPT-OSS-120B (120B total, 128 experts, ~13B active)

Hardware: Apple M4 Max 64GB — neither the original MXFP4 (63.5 GB) nor the mlx-community 4-bit affine (65.8 GB) fit on a 64GB machine. TurboQuant 3-bit is the only way to run this model on consumer hardware.

Step 1: Convert to TurboQuant 3-bit (recommended)

python -m turboquant_mlx.convert \
    --hf-path openai/gpt-oss-120b \
    --mlx-path ./gpt-oss-120b-tq3 \
    --bits 3 --group-size 64

Model size: 48 GB

Note: The default converter materializes the full quantized model in RAM before saving, so peak memory ≈ the quantized model size (~50–55 GB for a 120B). On a 64 GB machine that caps conversion at ~130B params. For anything larger, add --streaming: it writes each quantized layer to a shard and frees it, keeping peak memory to ~one 5 GB shard plus the layer being processed — so 200B+ models (Qwen3-235B, DeepSeek-V3) convert on a 64 GB Mac. Output is byte-identical to the in-memory path.

Step 2: Generate text

turboquant-generate \\
    --model ./gpt-oss-120b-tq3 \
    --prompt "Explain quantum computing in simple terms." \
    --max-tokens 200

Expected: ~44 tok/s generation, ~9.5 tok/s prefill, 52 GB peak memory on M4 Max 64GB

Step 3: Quick quality check

python -m turboquant_mlx.evaluate \
    --hf-path openai/gpt-oss-120b \
    --bits 3 \
    --no-affine --no-qjl \
    --num-samples 32 --seq-len 512

All bit-widths for GPT-OSS-120B

Method	Bits	Size	Peak RAM	Gen Speed	Fits 64 GB?	Quality
mlx-community 4-bit	4 (affine)	65.8 GB	—	—	No	—
MXFP4 (original)	4 (mxfp)	63.5 GB	~70 GB	—	No	—
TurboQuant	3	48 GB	52.3 GB	44 tok/s	Yes	Coherent, well-structured
TurboQuant	2	32 GB	34.9 GB	51 tok/s	Yes	Incoherent after ~20 tokens

Neither the original MXFP4 format (63.5 GB) nor the mlx-community affine 4-bit re-quantization (65.8 GB) fit on a 64GB Mac. TurboQuant 3-bit (48 GB) is the only way to run GPT-OSS-120B on consumer hardware — and at 44 tok/s, it's interactive speed. At 2-bit, the model fits easily but generation quality degrades rapidly — 3-bit is the minimum for coherent output on pre-quantized MoE models.

Qwen3.5-122B-A10B (122B total, 256 experts, 8 active, ~10B active)

Hardware: Apple M4 Max 64GB — the original BF16 model is ~240 GB. TurboQuant 3-bit compresses it to ~50 GB, fitting on a 64GB machine.

This is a brand-new architecture featuring 256 MoE experts (the most of any model we've tested), hybrid attention (GatedDeltaNet linear attention + standard softmax attention), and thinking/reasoning capability. The model also has a shared expert per layer alongside the routed experts.

Step 1: Convert to TurboQuant 3-bit

python -m turboquant_mlx.convert \
    --hf-path Qwen/Qwen3.5-122B-A10B \
    --mlx-path ./qwen3.5-122b-tq3 \
    --bits 3 --group-size 64

Model size: ~50 GB | Conversion time: ~90 seconds

Note: Conversion requires ~55 GB peak memory. Close all other applications before running. The converter uses memory-efficient processing — each expert layer is replaced immediately after quantization with aggressive garbage collection to handle the 256 experts per layer.

Step 2: Generate text

turboquant-generate \\
    --model ./qwen3.5-122b-tq3 \
    --prompt "Why is the sky blue? Explain in simple terms." \
    --max-tokens 200

Expected: ~26.5 tok/s generation, 55 GB peak memory on M4 Max 64GB

Benchmark

Method	Bits	Size	Peak RAM	Gen Speed	Fits 64 GB?	Quality
BF16 (original)	16	~240 GB	—	—	No	—
TurboQuant	3	~50 GB	54.9 GB	26.5 tok/s	Yes	Coherent reasoning with structured thinking

Qwen3.5-122B-A10B is the largest and most complex model TurboQuant has been tested on: 122B parameters, 256 experts (8 active per token), hybrid GatedDeltaNet + softmax attention, and a shared expert per MoE layer. At 3-bit, the model produces structured reasoning with proper analysis steps — demonstrating that TurboQuant preserves thinking capability at extreme compression.

Run it on a 16 GB Mac mini (expert streaming)

This 122B model — ~54 GB on disk — also runs on a 16 GB Mac mini via expert streaming (the same mechanism as Qwen3.6-35B-A3B). Only the router-selected experts are paged from disk per token (LRU-cached), so the resident footprint stays well under the machine's GPU wired-memory cap, and output is bit-identical to the fully-resident model. Requires turboquant-mlx-full>=0.4.1.

python -m turboquant_mlx.stream.stream_generate \
    --model manjunathshiva/qwen3.5-122b-tq3 \
    --prompt "Explain why the sky is blue." \
    --max-tokens 128 --cache-budget-gb 4

Measured on a base Apple M4 Mac mini, 16 GB:

Cache budget	Expert hit-rate	Disk read / token	Decode	Peak (mlx)
`--cache-budget-gb 1`	0%	~1.78 GB	~0.6 tok/s	6.0 GB
`--cache-budget-gb 4` (recommended)	44.6%	~0.93 GB	~1.1 tok/s	9.0 GB

On a 16 GB machine the binding limit is the Metal GPU wired-memory cap (~10.5 GB), not total RAM — and the expert cache counts against it, so mlx_peak ≈ 5 GB + cache_budget. --cache-budget-gb 4 is the sweet spot (~9 GB peak, safe margin); higher budgets risk a Metal out-of-memory error. Throughput is disk-bandwidth-bound (~10B active params/token) → ~1 tok/s on a single mini SSD. Slow, but a 122B model running on a 16 GB Mac is the result.

Qwen3.6-35B-A3B on a 16 GB Mac mini (expert streaming)

Hardware: Apple M4 Max 64GB to convert; runs fully resident on 64 GB or on a 16 GB Mac mini via expert streaming. Qwen3.6-35B-A3B is a hybrid linear-attention (qwen3_5_moe, qwen3_next-style) + MoE model — 256 routed experts (top-8) + 1 shared, ~35B total / ~3B active. The text-only language model is extracted (the vision tower is dropped during conversion).

A pre-converted 3-bit (group-size 32) model is on the Hub:

→ manjunathshiva/Qwen3.6-35B-A3B-tq3-g32 — ~16 GB on disk; ~60 tok/s at ~18 GB peak when fully resident on a 64 GB Mac.

Run it fully resident (64 GB)

turboquant-generate \
    --model manjunathshiva/Qwen3.6-35B-A3B-tq3-g32 \
    --prompt "Explain why the sky is blue." \
    --max-tokens 512

Run it on a 16 GB Mac mini (expert streaming)

The model is ~16 GB on disk, so it won't fit fully resident in 16 GB alongside the OS (resident decode peaks ~18 GB). Expert streaming pages only the router-selected experts from disk per token (LRU-cached), keeping resident memory to a few GB. Output is bit-identical to the fully-resident model. (os.pread + macOS F_NOCACHE keep the OS page cache from ballooning while streaming.)

Since v0.5.0 the missing experts for each layer are read in parallel on a thread pool (--prefetch-workers, default 8), hiding SSD latency behind compute — ~1.9× faster decode at a tight cache budget, still bit-identical. Pass --prefetch-workers 1 for the serial baseline.

python -m turboquant_mlx.stream.stream_generate \
    --model manjunathshiva/Qwen3.6-35B-A3B-tq3-g32 \
    --prompt "Explain why the sky is blue." \
    --max-tokens 512 --cache-budget-gb 8

Benchmark (base Apple M4 Mac mini, 16 GB)

Config	Expert hit-rate	Disk read / token	Decode	Peak RSS
`--cache-budget-gb 2`	~60%	~175 MB	~3.0 tok/s	3.9 GB
`--cache-budget-gb 8` (recommended)	91%	~41 MB	~4.5 tok/s	9.4 GB

A larger cache keeps more experts resident, raising the hit-rate and cutting SSD reads — the throughput limiter when streaming. --cache-budget-gb 8 is the sweet spot on a 16 GB machine; drop to 2 if RAM is tight. Streaming targets the SwitchGLU expert layout used by qwen3_5_moe and the DeepSeek MLA+MoE family (deepseek_v2/v3); the loader auto-detects the model's layer-key prefix.

Note: Qwen3.6 is a thinking-mode model — it emits a reasoning trace before the final answer, so give it a generous --max-tokens (512+) for tasks that need a concluding answer.

Tuning the streaming reader (`v0.6.1`)

Once the cache policy is reasonable, disk bandwidth is the wall — for MoE decode the LRU + 8-worker parallel-read pool is already near-optimal, so the big levers are faster storage (Thunderbolt/NVMe) and fewer bytes/token (a hybrid build, a bigger --cache-budget-gb), not the read algorithm. A few knobs squeeze the rest:

Knob	Default	What it does
`--max-active-experts K`	`4`	K-reduction — caps router `top_k` to `min(native, K)` per MoE block, so the switch streams fewer experts/token. `argpartition` selects fewer and `norm_topk_prob` renormalizes the gates, so it stays a clean reduced-K MoE. On Qwen3.6-35B-A3B (native top-8) K=8→4 is byte-identical on the 6-test stress harness and cuts streamed disk reads ~2.09× (1.4× faster decode in the disk-bound regime); K=2 collapses (broken JSON). `4` = safe floor; `0` = native routing.
`--use-page-cache` / `--no-page-cache`	auto	Trust-OS — whether expert reads use the OS page cache (vs `F_NOCACHE`). On a roomy machine where the model fits in free RAM, leaving the page cache on returns LRU-eviction re-reads from warm RAM instead of disk: 2.44× faster decode on the 35B at a small budget (7.58 → 18.50 tok/s), same hit-rate/RSS. Auto-enables only when model files are `< 0.6× total RAM`, so a 16 GB mini on a 70 GB MoE keeps `F_NOCACHE` and never thrashes.
read-coalescing	on	Merges contiguous missed experts into one `os.pread`. Bit-identical, free, ~5% faster when disk-bound. No flag.
`--prefetch-ahead N`	`0` (off)	Speculatively prefetch the next N layers' experts (predicted from the previous token's routing) on a background thread. ~+6% on fast NVMe with spare bandwidth; self-disables if the drive proves bandwidth-bound (e.g. a saturated USB bus), so it's safe to set `1`.
`--pin-file pin.json`	none	Keep a calibrated hot-expert set permanently resident. Experimental — measured net-negative vs pure LRU on a 122B (static pinning costs LRU's adaptivity). For experimentation only.

The --max-active-experts and page-cache levers are ports of Flash-MoE's K-reduction and "trust the OS" findings (blueprint: Apple LLM in a Flash), measured on the TurboQuant streaming path.

Generate pin.json (and a co-activation perm.json for the optional stream/repack_experts.py relayout) with python -m turboquant_mlx.stream.calibrate_experts.

Qwen3.6-27B (dense coding model for a 48 GB Mac)

Hardware: converts on a 64 GB Mac — or off slow USB storage with v0.6.2+, which forces the disk read ahead of GPU compute so conversion doesn't trip the Metal GPU watchdog — and runs fully resident on a 48 GB Mac with headroom to spare. Qwen/Qwen3.6-27B is a dense (qwen3_5) long-context coder — 64 layers, hybrid attention (48 GatedDeltaNet linear-attention + 16 full-attention layers), head_dim 256, 262K context — that Qwen positions as competitive on SWE-bench Verified / SWE-bench Pro. Being dense, it has no experts to stream: it loads once and stays in RAM, so storage only matters for load time.

A pre-converted 3-bit (group-size 32) build is on the Hub:

→ manjunathshiva/Qwen3.6-27B-tq3-g32 — ~13 GB on disk, ~17.5 GB peak at runtime (fits 48 GB with ~30 GB free for KV), ~14 tok/s decode.

Run it

turboquant-generate \
    --model manjunathshiva/Qwen3.6-27B-tq3-g32 \
    --prompt "Write a Python function that merges overlapping intervals." \
    --max-tokens 512 --temp 0.7

Serve it to Cursor / VS Code (OpenAI-compatible)

turboquant-serve --model manjunathshiva/Qwen3.6-27B-tq3-g32 --port 8080

Point the IDE's custom OpenAI base URL at http://localhost:8080/v1. Stock mlx_lm.server can't load a TurboQuant model (KeyError: 'turboquant') — turboquant-serve patches the loader so the weights load through the PolarQuant path.

Convert it yourself

python -m turboquant_mlx.convert \
    --hf-path Qwen/Qwen3.6-27B \
    --mlx-path ./Qwen3.6-27B-tq3-g32 \
    --bits 3 --group-size 32 --streaming

Note: Qwen3.6 is a thinking-mode model — it emits a reasoning trace before the answer, so give it a generous --max-tokens (512+). Only the 16 full-attention layers keep a growing KV cache (the 48 linear-attention layers use a fixed-size state), so it stays KV-light for long coding context; compress further with --kv-k-bits 8 --kv-v-bits 3.

Qwen3-235B-A22B-Instruct-2507 — a 235B MoE that converts on a 16 GB Mac (hybrid + streaming)

Hardware: converts on a 16 GB Mac mini via --streaming; runs on a 64 GB Mac (expert streaming) or fully resident on 96 GB+. Qwen3-235B-A22B is a qwen3_moe Mixture-of-Experts — 94 layers, 128 routed experts (top-8), ~235B total / ~22B active.

This is a hybrid tq3a-tq2e build: 3-bit attention (the always-on path, kept safer) + 2-bit experts (where the parameters — and the savings — live), routers full precision. The 128-expert / top-8 routing carries enough redundancy to absorb 2-bit experts cleanly — the same reason gpt-oss-120b holds at 2-bit while gpt-oss-20b (32 experts) collapses. Result: ~470 GB BF16 → 70.5 GB (15 shards, 6.7×).

A pre-converted build is on the Hub:

→ manjunathshiva/Qwen3-235B-A22B-Instruct-2507-tq3a-tq2e-g32

Convert it yourself (streaming, fits in ~8–12 GB RAM)

python -m turboquant_mlx.convert \
    --hf-path Qwen/Qwen3-235B-A22B-Instruct-2507 \
    --mlx-path /Volumes/SSD/qwen3-235b-tq3a-tq2e-g32 \
    --bits 3 --mlp-bits 2 -g 32 --streaming

--mlp-bits 2 drops experts to 2-bit while --bits 3 keeps attention at 3-bit; --streaming writes each quantized layer to a shard and frees it, so the full 235B converts in ~8–12 GB of RAM — it was produced on a 16 GB Mac mini in ~18 minutes. Point --mlx-path at a drive with ≥70 GB free.

Run it (expert streaming)

python -m turboquant_mlx.stream.stream_generate \
    --model manjunathshiva/Qwen3-235B-A22B-Instruct-2507-tq3a-tq2e-g32 \
    --prompt "Explain why the sky is blue." \
    --max-tokens 512 --cache-budget-gb 40

Quality + streaming benchmark

A 6-probe stress run passes 5/6: coherent long-form essay, correct multi-step math ($142.80 with a 15% bulk discount), correct memoized Fibonacci, strict-JSON formatting, and clean 1–15 enumeration. The one miss was exact factual recall — an in-context password came back with a single flipped digit (RAVEN-stone-91 → -51). Math/reasoning held; verify outputs where an exact literal value matters.

Need exact recall? The full-3-bit sibling Qwen3-235B-A22B-Instruct-2507-tq3-g32 (3-bit experts, 103 GB) fixes the needle flip and passes 6/6 — at ~1.3 tok/s / 86.3% hit-rate on a 64 GB Mac (slower and bigger than this hybrid; the cost of full 3-bit experts). Pick the hybrid for the smallest footprint, the tq3 build when exact literal recall matters.

Machine	Cache budget	Expert hit-rate	Disk read / token	Decode	Peak memory
M4 Mac mini, 16 GB	`--cache-budget-gb 6`	~38%	~3.2 GB	~0.2 tok/s	10.1 GB
64 GB Mac	`--cache-budget-gb 40`	94.1%	~0.28 GB	~4–6 tok/s (warm)	46 GB

On 64 GB a 40 GB cache holds ~60% of the ~67 GB of experts, but temporal locality lifts the hit-rate to 94.1%, so warm decode runs at the compute-bound ~4–6 tok/s. Throughput is bursty: the first generation and tasks that route into a colder slice of experts stall on the SSD until their experts page in. Bump sudo sysctl iogpu.wired_limit_mb=57344 to raise the cache past the ~48 GB default Metal wired cap.

Nemotron-3 (Mamba/attention hybrid)

Nemotron-3 is NVIDIA's hybrid Mamba2 + attention architecture. Two variants are tested:

Nano-4B — dense (Mamba + MLP + attention), 42 layers
Super-120B-A12B — hybrid MoE (Mamba + 512-expert latent-MoE + attention), 88 layers, ~12B active per token

Both require mlx-lm ≥ 0.31.3 for upstream Nemotron-H support (installed automatically).

Convert

# Nano-4B
python -m turboquant_mlx.convert \
    --hf-path nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 \
    --mlx-path ./nemotron-3-nano-4b-tq3 \
    --bits 3 --group-size 64

# Super-120B
python -m turboquant_mlx.convert \
    --hf-path nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 \
    --mlx-path ./nemotron-3-super-120b-tq3 \
    --bits 3 --group-size 64

Generate

Nemotron-3's chat template ends in a <think>\n scaffold that primes EOS as the top-1 logit at the start of the assistant turn. Pass --min-tokens to mask EOS for the first N tokens so the model enters the think phase:

turboquant-generate \\
    --model ./nemotron-3-super-120b-tq3 \
    --prompt "Why is the sky blue?" \
    --max-tokens 200 --min-tokens 50

Benchmarks (M4 Max)

Model	Bits	Size	Peak RAM	Gen Speed	Quality
Nemotron-3-Nano-4B	3	~2.2 GB	4.3 GB	75.6 tok/s	Coherent
Nemotron-3-Super-120B-A12B	3	~50 GB	54.7 GB	18.7 tok/s	Coherent with structured `<think>` reasoning (974-token answer w/ self-correction, formulas, formatted structure)
Nemotron-3-Super-120B-A12B (hybrid)	3-attn / 2-experts, gs=32	~36 GB	~40.8 GB	~27.2 tok/s	Coherent prose, code, format, and long-context recall; math accuracy degraded — see Phase-1 note below

48 GB-RAM target: hybrid (3-bit attention / 2-bit experts) at group-size 32

The standard 3-bit Super-120B (~50 GB) needs ~55 GB peak and only fits a 64 GB Mac after raising iogpu.wired_limit_mb. For users on a 64 GB Mac who want headroom for other applications — or for users on 48 GB Macs — there is a hybrid quantization that keeps attention at 3-bit (where precision matters most) and pushes experts to 2-bit (where the bulk of the weights live), at a smaller group size (g=32) that improves per-group fit.

Pre-converted model on Hugging Face:

hf download manjunathshiva/Nemotron-3-Super-120B-A12B-tq3a-tq2e-g32 \
    --local-dir ~/models/nemotron-3-super-120b-tq3a-tq2e-g32

→ manjunathshiva/Nemotron-3-Super-120B-A12B-tq3a-tq2e-g32 on the Hub: ~36 GB on disk, ~40.8 GB peak memory, ~27.2 tok/s decode, fits the default 48 GB iogpu.wired_limit_mb cap.

Or convert from BF16 source yourself:

python -m turboquant_mlx.convert \
    --hf-path nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 \
    --mlx-path ./nemotron-3-super-120b-tq3a-tq2e-g32 \
    --bits 2 --attn-bits 3 --mlp-bits 2 --group-size 32

For long-form generation, the model needs a small repetition penalty to avoid degenerate tail loops at >1500 tokens. The recommended decode config (empirically validated to keep essay, code, format, and long-context recall clean):

turboquant-generate \\
    --model ./nemotron-3-super-120b-tq3a-tq2e-g32 \
    --prompt "Why is the sky blue?" \
    --max-tokens 4096 --min-tokens 50 \
    --temp 0.7 --rep-penalty 1.04 --rep-ctx 256

Phase-1 known limitation: math accuracy. Step-by-step arithmetic on the hybrid degrades under any non-zero --rep-penalty. For numeric/math prompts in Phase 1, omit --rep-penalty (you may see long-gen tail loops on long prompts, but the arithmetic will land correctly more often). A permanent fix is planned for Phase 2 — likely first/last-layer bit protection, a calibration-data codebook, or a fused QJL Metal kernel. Until Phase 2, use the hybrid for prose, coding, format, and long-context tasks; use the standard 3-bit model for serious numeric work.

The fused MoE decode kernel transparently chunks expert routings on long prompts, so this hybrid handles long-context retrieval (e.g. password- recall over 4000+ tokens of context) without the kernel argument-validation crash that affected earlier builds.

How It Works

TurboQuant is a two-stage, calibration-free quantization pipeline:

Hadamard Rotation — Multiply weights by a randomized Hadamard matrix, transforming any weight distribution into a near-Gaussian shape. This is data-oblivious (no calibration data needed).
Lloyd-Max Codebook — Apply information-theoretically optimal quantization for Gaussian distributions. The codebook is a mathematical constant, precomputed once.

The result: near-zero quality loss at 3-bit, and usable 2-bit quantization where standard affine completely breaks down.

For MoE models, all experts within a layer share the same rotation signs and codebook, keeping storage efficient.

CLI Options

python -m turboquant_mlx.convert --help

Options:
  --hf-path TEXT       HuggingFace model path or local path (required)
  --mlx-path TEXT      Output directory (default: mlx_model)
  --bits {2,3,4}       Quantization bit-width (default: 3)
  --group-size {32,64,128}  Elements per quantization group (default: 64)
  --rotation TEXT      Rotation method: hadamard, blockwise_hadamard, none
  --use-qjl           Enable 1-bit QJL residual correction (+1 bit overhead)
  --dtype TEXT         Model dtype before quantization: float16, bfloat16

Supported Architectures

Architecture	Model Type	MoE	Status
LLaMA / Llama 3	`llama`	No	Tested
Qwen2 / Qwen2.5	`qwen2`	No	Tested
Qwen3.5	`qwen3_5`	No	Tested
Mistral	`mistral`	No	Tested
Qwen1.5-MoE	`qwen2_moe`	Yes	Tested
GPT-OSS	`gpt_oss`	Yes	Tested
Qwen3.5-MoE / Qwen3.6-35B-A3B	`qwen3_5_moe`	Yes (256 experts)	Tested (122B, 35B-A3B); 35B streams on a 16 GB Mac mini
Qwen3-MoE	`qwen3_moe`	Yes (128 experts, top-8)	Tested — Qwen3-235B-A22B converted to a hybrid tq3a-tq2e build (70.5 GB) on a 16 GB Mac mini via `--streaming`; streams and passes 5/6 quality probes on a 64 GB Mac
Nemotron-H (Mamba/attention hybrid)	`nemotron_h`	Yes (512 experts w/ latent MoE on Super-120B)	Tested (Nano-4B, Super-120B) — requires mlx-lm ≥ 0.31.3
DeepSeek-V2 / V3 (MLA + MoE)	`deepseek_v2` / `deepseek_v3` / `deepseek_v32`	Yes (SwitchGLU experts)	Tested (V2-Lite: convert + resident + streaming, coherent at 3-bit); V3/V3.2 share the MLA+MoE layout and reuse the config (untested — need ~250 GB disk)
DiffusionGemma (block-diffusion MoE, via mlx-vlm)	`diffusion_gemma`	Yes (128 experts, top-8)	Tested (26B-A4B: convert + block-diffusion sampler, coherent at 3-bit — HF). Experimental: decode is much slower than native 4-bit until a batched codebook gather-GEMM kernel lands

mlx-vlm architectures (multimodal / diffusion)

Architectures that live in mlx-vlm rather than mlx-lm convert and run through dedicated entry points (v0.7.0+):

pip install "turboquant-mlx-full[vlm]"   # adds mlx-vlm >= 0.6.3

# Convert (vision towers, routers, and known quant-sensitive blocks stay full precision)
python -m turboquant_mlx.convert_vlm \
    --hf-path google/diffusiongemma-26B-A4B-it \
    --mlx-path ./diffusiongemma-26B-A4B-it-tq3-g32 --bits 3 -g 32

# Generate (runs mlx-vlm's sampler — block-diffusion denoising for DiffusionGemma)
python -m turboquant_mlx.generate_vlm \
    --model ./diffusiongemma-26B-A4B-it-tq3-g32 \
    --prompt "Write a short paragraph about the ocean." --max-tokens 256

Project Structure

turboquant_mlx/
    config.py                 # TurboQuantConfig
    convert.py                # CLI: HF model -> TurboQuant MLX
    generate.py               # Text generation with TurboQuant models
    evaluate.py               # Perplexity evaluation
    quantize_model.py         # Model traversal & layer replacement
    demo_kv.py                # Streaming generation demo with KV cache compression
    test_kv_cache.py          # KV cache roundtrip + integration tests
    core/
        codebook.py           # Lloyd-Max codebooks for Gaussian
        rotation.py           # Randomized Hadamard rotation
        polar_quantize.py     # Rotate + codebook quantize
        packing.py            # Bit-packing into uint32
        qjl.py                # QJL residual correction
    layers/
        polar_linear.py       # PolarQuantizedLinear (dense)
        polar_switch_linear.py # PolarQuantizedSwitchLinear (MoE)
        polar_kv_cache.py     # TurboQuantKVCache (runtime KV compression)
    kernels/
        polar_qmv.py          # Fused Metal kernel (dense decode)
        polar_gather_qmv.py   # Fused Metal kernel (MoE shared input)
        polar_multi_gather_qmv.py  # Fused Metal kernel (MoE per-expert input)
    integration/
        rotation_configs.py   # Per-architecture rotation configs
    stream/                   # Expert streaming — run MoE models beyond RAM (v0.4.0)
        safetensors_reader.py # Per-expert disk slice reads (os.pread + F_NOCACHE; coalesced ranges)
        streaming_switch.py   # StreamingSwitchLinear + byte-budgeted LRU ExpertCache (+ prefetch/pin)
        loader.py             # load_streaming(): swap experts to streaming after lazy load
        stream_generate.py    # CLI: stream-generate (--cache-budget-gb, --prefetch-ahead, --pin-file)
        calibrate_experts.py  # Routing trace → pin.json (hot experts) + perm.json (co-activation)
        repack_experts.py     # Optional co-activation on-disk relayout (byte-identical)

Citation

@misc{turboquant_mlx,
    title={TurboQuant-MLX: Extreme Weight and KV Cache Compression for Apple Silicon},
    year={2025},
    note={MLX implementation of TurboQuant (Zandieh et al., 2025) for both weight quantization and runtime KV cache compression}
}

License

MIT

Acknowledgments

TurboQuant — Zandieh, Daliri, Hadian, Mirrokni (2025)
MLX — Apple Machine Learning Research
mlx-lm — MLX language model utilities

Name		Name	Last commit message	Last commit date
Latest commit History 50 Commits
.github		.github
benchmarks		benchmarks
core		core
docs/design		docs/design
integration		integration
kernels		kernels
layers		layers
stream		stream
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
LICENSE-MIT		LICENSE-MIT
MANIFEST.in		MANIFEST.in
NOTICE		NOTICE
README.md		README.md
SECURITY.md		SECURITY.md
__init__.py		__init__.py
__main__.py		__main__.py
compat.py		compat.py
config.py		config.py
convert.py		convert.py
convert_streaming.py		convert_streaming.py
convert_vlm.py		convert_vlm.py
demo_kv.py		demo_kv.py
evaluate.py		evaluate.py
generate.py		generate.py
generate_vlm.py		generate_vlm.py
pyproject.toml		pyproject.toml
quantize_model.py		quantize_model.py
sampling.py		sampling.py
serve.py		serve.py
test_kv_cache.py		test_kv_cache.py

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TurboQuant-MLX

Key Results — Weight Compression

Key Results — KV Cache Compression

Key Results — Apple M5 Pro (48 GB, Metal4)

Install

Requirements

Install from source (for development)

Quick Start

1. Convert a model to TurboQuant format

2. Generate text

3. Evaluate perplexity

4. Generate with KV cache compression

5. Serve a TurboQuant model over an OpenAI-compatible API

Serve a model bigger than RAM (expert streaming)

Memory tuning when serving near the unified-memory ceiling

Compress the KV cache while serving

KV Cache Compression

Programmatic usage

Choosing a bit-width (v0.2)

CLI flags

The speed flip

Compatibility

Running GPT-OSS MoE Models on Apple Silicon

GPT-OSS-20B (21B total, 32 experts, 3.6B active)

Step 1: Convert to TurboQuant 3-bit (recommended)

Step 2: Generate text

Step 3: Run a quick quality check

All bit-widths for GPT-OSS-20B

GPT-OSS-120B (120B total, 128 experts, ~13B active)

Step 1: Convert to TurboQuant 3-bit (recommended)

Step 2: Generate text

Step 3: Quick quality check

All bit-widths for GPT-OSS-120B

Qwen3.5-122B-A10B (122B total, 256 experts, 8 active, ~10B active)

Step 1: Convert to TurboQuant 3-bit

Step 2: Generate text

Benchmark

Run it on a 16 GB Mac mini (expert streaming)

Qwen3.6-35B-A3B on a 16 GB Mac mini (expert streaming)

Run it fully resident (64 GB)

Run it on a 16 GB Mac mini (expert streaming)

Benchmark (base Apple M4 Mac mini, 16 GB)

Tuning the streaming reader (v0.6.1)

Qwen3.6-27B (dense coding model for a 48 GB Mac)

Run it

Serve it to Cursor / VS Code (OpenAI-compatible)

Convert it yourself

Qwen3-235B-A22B-Instruct-2507 — a 235B MoE that converts on a 16 GB Mac (hybrid + streaming)

Convert it yourself (streaming, fits in ~8–12 GB RAM)

Run it (expert streaming)

Quality + streaming benchmark

Nemotron-3 (Mamba/attention hybrid)

Convert

Generate

Benchmarks (M4 Max)

48 GB-RAM target: hybrid (3-bit attention / 2-bit experts) at group-size 32

How It Works

CLI Options

Supported Architectures

mlx-vlm architectures (multimodal / diffusion)

Project Structure

Citation

License

Acknowledgments

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Tuning the streaming reader (`v0.6.1`)

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