Model benchmarks
Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head local LLM performance
As of September 2026, Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head runs at up to 69.1 tok/s for local inference (best of 4 community benchmark runs across 1 GPU).
Model size
Unknown
Peak speed
69.1 tok/s
Average speed
63.8 tok/s
Avg PP
384.9 tok/s
Min memory
99.3 GB
Max context
262,144 tokens
Avg output / run
13,053 tokens
Avg runtime / run
3m 58s
Avg quality
81.1
Benchmark runs
4
GPUs tested
1
Quality by task
Average LLM-judged quality (0–100) with the run-to-run spread shown as a P5–P95 band, overall and for each benchmark task, across all 4 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.
| Task | P5 (low) | Avg | P95 (high) |
|---|---|---|---|
| Overall | 76.0 | 81.1 | 84.2 |
| Agent Workflow | 78.4 | 85.2 | 91.3 |
| Code Generation | 62.9 | 70.3 | 74.7 |
| Role Play & Narrative | 82.7 | 87.0 | 90.2 |
| Research & Analysis | 79.2 | 82.0 | 83.9 |
Performance by hardware and tool
Every hardware/tool/quantization combination Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| Apple M4 Max | oMLX | — | 69.1 tok/s | 63.8 tok/s | 99.3 GB | 262,144 tokens | 81.1 | 4 |
Benchmark runs
All 4 Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head good for coding?
- In our benchmarks, Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head scores 70.3/100 for coding. It runs at about 63.8 tok/s, so if you want more speed, ornith-ai/Ornith-1.5-35B-A3B-GGUF is faster (~90.8 tok/s) and still scores well for coding (83.8/100).
- Is Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head good for agentic (tool-using) tasks?
- In our benchmarks, Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head scores 85.2/100 for agentic workflows. It runs at about 63.8 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).
- How fast is Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head for local inference?
- Across 4 community benchmark runs, Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head reaches up to 69.1 tok/s and averages 63.8 tok/s, with the fastest results on Apple M4 Max.
- How much memory does Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head need?
- The leanest observed configuration used about 99.3 GB of memory.
- Which tools have been used to run Qwen3.8-Flash-Next-GPTQ6Experts-Q6Attn-BF16-PLE-MLX-MTP-Q8-GPTQ4Head?
- Benchmarks were submitted using oMLX. Results are community-contributed and updated as new runs arrive.