Instructions to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed with MLX:

# 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-Optimized-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)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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 Optimized Speed

Dynamic 4-bit quant with 8-bit attention. Higher quality and slightly slower. Recommended.

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. This is the recommended build: the Qwen Sparse Attention projections are kept at 8-bit, so the attention pathway that steers long contexts keeps its precision. For the absolute fastest build, pick Bare Speed.

The 32 GB n-gram table streams from SSD, so only the weights stay in memory. The pack peaks at 87 GiB while serving, so it is recommended for Macs with 128 GB or more. On a 96 GB Mac, use Bare Speed, which peaks at 78 GiB.

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.

Recommended for Apple Silicon Macs with 128 GB of unified memory or more: MacBook Pro M4 Max and M5 Max with 128 GB, and Mac Studio M3 Ultra, M4 Max and M5 Max with 128 GB or more. From MTPLX 2.12.0 it is the first recommendation on Macs with 256 GB or more. 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)	73.5
Same task, plain autoregressive	43.8

That is a 1.7x speculative multiplier through the product serve path, on sampled output that follows the model's own distribution.

How it is built

MoE experts and dense matrices at 4-bit with 64-weight groups; the Qwen Sparse Attention projections promoted to 8-bit (the quality edge over Bare Speed).
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.safetensors sidecar that MTPLX streams from SSD on every Mac. The vision tower is preserved in the weights.


Download	115.1 GB (includes the 32 GB n-gram table)
Resident weights (n-gram on SSD)	~83 GB + working set
Peak while serving	87 GiB
Recommended Macs	128 GB+ unified memory (on 96 GB, use Bare Speed)
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 Optimized Speed".

Command line:

pip install mtplx
mtplx serve --model Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed

Sibling: Bare Speed (flat 4-bit, the quickest build).

Base model: Qwen/Qwen3.8-Flash-Next (Qwen Community License; the upstream model card is preserved in this repo as README-upstream-qwen.md).

Downloads last month: 16,702

Safetensors

Model size

126B params

Tensor type

U32

BF16

I64

MLX

Hardware compatibility

4-bit

Model tree for Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Speed

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

(269)

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