Model benchmarks

Qwen3.8-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8 local LLM performance

As of September 2026, Qwen3.8-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8 runs at up to 59.1 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).

oMLX
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Model size

Unknown

Peak speed

59.1 tok/s

Average speed

57.5 tok/s

Avg prefill

400.1 tok/s

Min memory

92.0 GB

Max context

262,144 tokens

Avg output / run

36,104 tokens

Avg runtime / run

11m

Avg quality

83.4

Benchmark runs

2

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 2 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.

TaskP5 (low)AvgP95 (high)
Overall82.283.484.6
Agent Workflow83.384.385.3
Code Generation59.567.876.1
Role Play & Narrative93.493.793.9
Research & Analysis85.888.090.1

Performance by hardware and tool

Every hardware/tool/quantization combination Qwen3.8-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8 has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
Apple M4 MaxoMLX59.1 tok/s57.5 tok/s92.0 GB262,144 tokens83.42

Frequently asked questions

Is Qwen3.8-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8 good for coding?
In our benchmarks, Qwen3.8-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8 scores 67.8/100 for coding. It runs at about 57.5 tok/s, so if you want more speed, unsloth/Qwen3.8-27B-GGUF:IQ3_S is faster (~104.3 tok/s) and still scores well for coding (83.0/100).
Is Qwen3.8-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8 good for agentic (tool-using) tasks?
In our benchmarks, Qwen3.8-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8 scores 84.3/100 for agentic workflows. It runs at about 57.5 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-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8 for local inference?
Across 2 community benchmark runs, Qwen3.8-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8 reaches up to 59.1 tok/s and averages 57.5 tok/s, with the fastest results on Apple M4 Max.
How much memory does Qwen3.8-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8 need?
The leanest observed configuration used about 92.0 GB of memory.
Which tools have been used to run Qwen3.8-Flash-Next-Q5GU-g64-Q6Down-g128-Q6Attn-BF16-PLE-MLX-MTP-Q8?
Benchmarks were submitted using oMLX. Results are community-contributed and updated as new runs arrive.