Is Qwen3.8-27B-oQ8e-fp16-mtp good for coding?

In our benchmarks, Qwen3.8-27B-oQ8e-fp16-mtp scores 73.0/100 for coding. It runs at about 34.0 tok/s, so if you want more speed, local-qwen is faster (~60.2 tok/s) and still scores well for coding (84.8/100).

Is Qwen3.8-27B-oQ8e-fp16-mtp good for agentic (tool-using) tasks?

In our benchmarks, Qwen3.8-27B-oQ8e-fp16-mtp scores 79.7/100 for agentic workflows. It runs at about 34.0 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).

How fast is Qwen3.8-27B-oQ8e-fp16-mtp for local inference?

Across 17 community benchmark runs, Qwen3.8-27B-oQ8e-fp16-mtp reaches up to 35.5 tok/s and averages 34.0 tok/s, with the fastest results on Apple M2 Ultra.

How much memory does Qwen3.8-27B-oQ8e-fp16-mtp need?

The leanest observed configuration used about 33.3 GB of memory (quantizations tested: FP16).

Which tools have been used to run Qwen3.8-27B-oQ8e-fp16-mtp?

Benchmarks were submitted using oMLX. Results are community-contributed and updated as new runs arrive.

Model benchmarks

Qwen3.8-27B-oQ8e-fp16-mtp local LLM performance

As of September 2026, Qwen3.8-27B-oQ8e-fp16-mtp runs at up to 35.5 tok/s for local inference (best of 17 community benchmark runs across 2 GPUs).

oMLXFP16

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Model size

27B

Peak speed

35.5 tok/s

Average speed

34.0 tok/s

Avg PP

243.3 tok/s

Min memory

33.3 GB

Max context

262,144 tokens

Avg output / run

25,963 tokens

Avg runtime / run

15m 58s

Avg quality

81.6

Benchmark runs

GPUs tested

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 17 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	69.1	81.6	87.4
Agent Workflow	60.4	79.7	91.0
Code Generation	52.7	73.0	81.0
Role Play & Narrative	78.0	88.9	94.8
Research & Analysis	78.3	84.8	89.4

Performance by hardware and tool

Every hardware/tool/quantization combination Qwen3.8-27B-oQ8e-fp16-mtp 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 M2 Ultra	oMLX	FP16	35.5 tok/s	34.1 tok/s	59.1 GB	262,144 tokens	80.7	13
Apple M5 Max	oMLX	FP16	35.4 tok/s	33.7 tok/s	33.3 GB	262,144 tokens	84.6	4

Benchmark runs

All 17 Qwen3.8-27B-oQ8e-fp16-mtp runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is Qwen3.8-27B-oQ8e-fp16-mtp good for coding?: In our benchmarks, Qwen3.8-27B-oQ8e-fp16-mtp scores 73.0/100 for coding. It runs at about 34.0 tok/s, so if you want more speed, local-qwen is faster (~60.2 tok/s) and still scores well for coding (84.8/100).
Is Qwen3.8-27B-oQ8e-fp16-mtp good for agentic (tool-using) tasks?: In our benchmarks, Qwen3.8-27B-oQ8e-fp16-mtp scores 79.7/100 for agentic workflows. It runs at about 34.0 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).
How fast is Qwen3.8-27B-oQ8e-fp16-mtp for local inference?: Across 17 community benchmark runs, Qwen3.8-27B-oQ8e-fp16-mtp reaches up to 35.5 tok/s and averages 34.0 tok/s, with the fastest results on Apple M2 Ultra.
How much memory does Qwen3.8-27B-oQ8e-fp16-mtp need?: The leanest observed configuration used about 33.3 GB of memory (quantizations tested: FP16).
Which tools have been used to run Qwen3.8-27B-oQ8e-fp16-mtp?: Benchmarks were submitted using oMLX. Results are community-contributed and updated as new runs arrive.