Instructions to use mlx-community/Qwen3.8-Flash-Next-oQ8e-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Qwen3.8-Flash-Next-oQ8e-mtp with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3.8-Flash-Next-oQ8e-mtp mlx-community/Qwen3.8-Flash-Next-oQ8e-mtp
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Qwen3.8-Flash-Next-oQ8e-mtp
This model was quantized using oQ in oMLX v0.6.4. oQe (imatrix). MTP kept.
Quantization details
- Model type: qwen4_exp
- Bits: 8
- Group size: 64
- Format: MLX safetensors
- oMLX: Jundot v0.6.4 (source)
- Parameters: 125B total, 6B activated, 51B n-gram embedding, 4B MTP
- MTP: preserved (76
mtp.*tensors) - Source: Qwen/Qwen3.8-Flash-Next, converted to MLX, then oQe with
preserve_mtp
Benchmark
Apple M3 Ultra, 256 GB. oMLX 0.6.4. Thinking off, temperature 0, Lightning MTP depth 3. oQ4e is Jundot/Qwen3.8-Flash-Next-oQ4e-mtp on the same machine.
Write speed is how fast the reply is generated (generation_tokens_per_second). Read speed is how fast the prompt is ingested (prompt_tokens_per_second).
Coding task: write a Python Fibonacci function, no comments. All three packs stopped on their own.
| oQ4e | oQ6e | oQ8e | |
|---|---|---|---|
| Time to completion | 1.74 s | 2.07 s | 2.57 s |
| Time to first token | 0.51 s | 0.64 s | 0.67 s |
| Tokens written | 79 | 80 | 80 |
| Write speed (tok/s) | 64.4 | 56.1 | 42.1 |
| Read speed (tok/s) | 51.2 | 40.3 | 38.9 |
Longer prompts (256-token generation cap):
Write speed (tok/s)
| Context | oQ4e | oQ6e | oQ8e |
|---|---|---|---|
| Short code | 64.4 | 56.1 | 42.1 |
| 4K | 56.5 | 44.9 | 41.9 |
| 8K | 56.3 | 44.8 | 43.7 |
| 16K | 52.9 | 48.3 | 47.5 |
Read speed (tok/s)
| Context | oQ4e | oQ6e | oQ8e |
|---|---|---|---|
| Short code | 51.2 | 40.3 | 38.9 |
| 4K | 732 | 597 | 581 |
| 8K | 772 | 728 | 716 |
| 16K | 729 | 785 | 763 |
Raw timings: bench.json (this pack) and bench-compare.json (all three).
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Model size
180B params
Tensor type
U32
路
BF16 路
I64 路
Hardware compatibility
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8-bit
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Base model
Qwen/Qwen3.8-Flash-Next