MTPLX: the fastest way to run Qwen 3.8 on a Mac. Native multi-token-prediction speculative decoding on Apple Silicon, two to three times the speed of plain decoding, exact at any temperature.
Qwen 3.8 Flash-Next Bare Speed
Flat 4-bit quantization. Quickest Flash-Next speeds for chat and coding.
Qwen's 125B-A6B Flash-Next preview, the Qwen4-generation architecture with GDN hybrid MoE, Qwen Sparse Attention, and the 51B-parameter n-gram memory, running natively on MTPLX from day 0, with its native multi-token-prediction head drafting through MTPLX's speculative path. Every expert at flat 4-bit for the fastest Flash-Next build. If you want the higher- quality sibling, pick Optimized Speed.
The 32 GB n-gram table streams from SSD, so only the weights stay in memory. The pack peaks at 78 GiB while serving, which makes it the Flash-Next build for 96 GB Macs.
Measured on MTPLX 2.11.3 (16 September 2026)
This is the Qwen3.8-Flash-Next MLX pack for MTPLX, the fastest way to run Qwen 3.8 Flash Next on a Mac. MacBook Pro M5 Max with 128 GB, fans verified at maximum, sampled at the model's own settings (temperature 1.0, top-p 0.95, top-k 20). Conditions and sources for every row: mtplx.com/benchmarks.
| Run | tok/s |
|---|---|
| One OpenCode request on the Optimized Speed pack: 1,301 tokens generated, 18,539-token prompt with 18,364 tokens served from cache, MTP depth 3 | 125.8 |
| 9k-token code prompt, 1,500 tokens generated, seeded sampler, thinking off (62.5 on MTPLX 2.11.2) | 79.3 |
| 109k-token OpenCode turn, mean of two runs | 61.8 |
| 200k-token OpenCode turn, warm, mean of two runs | 50.3 |
| Full 45k to 56k-token generations (Flappy Bird at effort xhigh), whole turn | 66.8 |
Exactness on this release: a thousand four-token draws from the fast path match a thousand from the plain path within the plain path's own noise at every joint length, at temperature 1, top-p 0.95, top-k 20. A 96,760-token conversation restored from the session cache in 8 ms. 261,120-token prompts decode. Details: MTPLX 2.11.3 release notes.
Runs on Apple Silicon Macs with 96 GB of unified memory or more: MacBook Pro M4 Max and M5 Max with 128 GB, Mac Studio M3 Ultra, M4 Max and M5 Max. Guide: Run Qwen 3.8 Flash Next on a Mac.
Speeds
Measured on an M5 Max, fans verified at max, single stream, real server
(mtplx serve), official Qwen 3.8 sampling (temperature 1.0, top-p 0.95,
top-k 20, sampled, not greedy).
| Run | tok/s |
|---|---|
| Coding task, MTP speculative decode (the default) | 75.9 |
| Same task, plain autoregressive | 47.0 |
That is a 1.6x speculative multiplier through the product serve path, on sampled output that follows the model's own distribution.
How it is built
- Every MoE expert and dense matrix at 4-bit with 64-weight groups. Nothing promoted, this is the flat, fastest build.
- The GDN convolution and recurrent-state parameters, every norm, the QSA indexer, and the MTP head stay 16-bit.
- The n-gram embedding table ships as a separate
ngram-table.safetensorssidecar that MTPLX streams from SSD on every Mac. The vision tower is preserved in the weights.
| Download | 106.3 GB (includes the 32 GB n-gram table) |
| Resident weights (n-gram on SSD) | ~74 GB + working set |
| Peak while serving | 78 GiB |
| Recommended Macs | 96 GB+ unified memory |
| Context window | 262,144 tokens |
| MTP depth | adaptive, ceiling 3 |
| Sampling | temperature 1.0, top-p 0.95, top-k 20 (the official Qwen 3.8 contract) |
The serving contract ships inside mtplx_runtime.json. MTPLX reads it on
load. Drafts are accepted with the probability-ratio rule plus residual
resampling, so the output follows the model's own distribution at any
temperature.
Use it
Mac app: download at mtplx.com, pick "Qwen 3.8 Flash-Next Bare Speed".
Command line:
pip install mtplx
mtplx serve --model Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed
Sibling: Optimized Speed (dynamic quant with 8-bit attention, higher quality, slightly slower).
Base model: Qwen/Qwen3.8-Flash-Next
(Qwen Community License; the upstream model card is preserved in this repo as
README-upstream-qwen.md).
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Model tree for Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed
Base model
Qwen/Qwen3.8-Flash-Next
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Youssofal/Qwen3.8-Flash-Next-MTPLX-Bare-Speed") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True)