Is qwen3.8:27b-mlx good for coding?

In our benchmarks, qwen3.8:27b-mlx scores 66.2/100 for coding. It runs at about 25.0 tok/s, so if you want more speed, Nex-N2.5-mini-IQ3_XXS is faster (~80.3 tok/s) and still scores well for coding (84.5/100).

Is qwen3.8:27b-mlx good for agentic (tool-using) tasks?

In our benchmarks, qwen3.8:27b-mlx scores 80.0/100 for agentic workflows. It runs at about 25.0 tok/s, so if you want more speed, muse-glimmer:latest is faster (~33.3 tok/s) and still scores well for agentic workflows (91.6/100).

How fast is qwen3.8:27b-mlx for local inference?

Across 8 community benchmark runs, qwen3.8:27b-mlx reaches up to 40.3 tok/s and averages 25.0 tok/s, with the fastest results on Apple M5 Max.

How much memory does qwen3.8:27b-mlx need?

The leanest observed configuration used about 20.4 GB of memory (quantizations tested: nvfp4).

Which tools have been used to run qwen3.8:27b-mlx?

Benchmarks were submitted using LM Studio, Ollama. Results are community-contributed and updated as new runs arrive.

Model benchmarks

qwen3.8:27b-mlx local LLM performance

As of September 2026, qwen3.8:27b-mlx runs at up to 40.3 tok/s for local inference (best of 8 community benchmark runs across 4 GPUs).

LM StudioOllamanvfp4

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Model size

27.8B

Peak speed

40.3 tok/s

Average speed

25.0 tok/s

Min memory

20.4 GB

Max context

128,000 tokens

Avg output / run

47,057 tokens

Avg runtime / run

19m 11s

Avg quality

80.2

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 8 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	71.6	80.2	84.6
Agent Workflow	72.1	79.9	86.5
Code Generation	27.8	66.2	78.9
Role Play & Narrative	84.2	91.1	95.1
Research & Analysis	79.1	83.6	86.9

Performance by hardware and tool

Every hardware/tool/quantization combination qwen3.8:27b-mlx 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 M5 Max	Ollama	nvfp4	40.3 tok/s	35.1 tok/s	20.8 GB	128,000 tokens	83.2	2
Apple M5 Pro	Ollama	nvfp4	36.7 tok/s	25.1 tok/s	21.3 GB	32,768 tokens	80.4	2
Apple M4 Pro	Ollama	nvfp4	28.6 tok/s	22.5 tok/s	20.4 GB	32,768 tokens	81.0	2
Apple M5 Max	LM Studio	—	22.2 tok/s	22.2 tok/s	n/a	32,768 tokens	67.5	1
Apple M2 Pro	Ollama	nvfp4	12.3 tok/s	12.3 tok/s	20.5 GB	65,536 tokens	85.0	1

Benchmark runs

All 8 qwen3.8:27b-mlx 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-mlx good for coding?: In our benchmarks, qwen3.8:27b-mlx scores 66.2/100 for coding. It runs at about 25.0 tok/s, so if you want more speed, Nex-N2.5-mini-IQ3_XXS is faster (~80.3 tok/s) and still scores well for coding (84.5/100).
Is qwen3.8:27b-mlx good for agentic (tool-using) tasks?: In our benchmarks, qwen3.8:27b-mlx scores 80.0/100 for agentic workflows. It runs at about 25.0 tok/s, so if you want more speed, muse-glimmer:latest is faster (~33.3 tok/s) and still scores well for agentic workflows (91.6/100).
How fast is qwen3.8:27b-mlx for local inference?: Across 8 community benchmark runs, qwen3.8:27b-mlx reaches up to 40.3 tok/s and averages 25.0 tok/s, with the fastest results on Apple M5 Max.
How much memory does qwen3.8:27b-mlx need?: The leanest observed configuration used about 20.4 GB of memory (quantizations tested: nvfp4).
Which tools have been used to run qwen3.8:27b-mlx?: Benchmarks were submitted using LM Studio, Ollama. Results are community-contributed and updated as new runs arrive.