Is unsloth/Qwen3.8-27B-GGUF:Q4_K_M good for coding?

In our benchmarks, unsloth/Qwen3.8-27B-GGUF:Q4_K_M scores 78.1/100 for coding. It runs at about 95.3 tok/s, so if you want more speed, Qwen3.6-35B-A3B-Q4_K_XL is faster (~156.9 tok/s) and still scores well for coding (82.2/100).

Is unsloth/Qwen3.8-27B-GGUF:Q4_K_M good for agentic (tool-using) tasks?

In our benchmarks, unsloth/Qwen3.8-27B-GGUF:Q4_K_M scores 85.9/100 for agentic workflows. It runs at about 95.3 tok/s, so if you want more speed, qwen3.8-flash-next-iq3_s is faster (~116.8 tok/s) and still scores well for agentic workflows (88.4/100).

How fast is unsloth/Qwen3.8-27B-GGUF:Q4_K_M for local inference?

Across 6 community benchmark runs, unsloth/Qwen3.8-27B-GGUF:Q4_K_M reaches up to 99.3 tok/s and averages 95.3 tok/s, with the fastest results on NVIDIA GeForce RTX 4090.

Which tools have been used to run unsloth/Qwen3.8-27B-GGUF:Q4_K_M?

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

Model benchmarks

unsloth/Qwen3.8-27B-GGUF:Q4_K_M local LLM performance

As of August 2026, unsloth/Qwen3.8-27B-GGUF:Q4_K_M runs at up to 99.3 tok/s for local inference (best of 6 community benchmark runs across 1 GPU).

llama.cppQ4_K_M

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Model size

27B

Peak speed

99.3 tok/s

Average speed

95.3 tok/s

Min memory

n/a

Max context

131,072 tokens

Avg output / run

68,672 tokens

Avg runtime / run

11m 8s

Avg quality

85.1

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 6 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	82.9	85.1	87.1
Agent Workflow	82.2	85.9	88.9
Code Generation	72.4	78.1	83.9
Role Play & Narrative	86.3	89.0	92.0
Research & Analysis	84.4	87.4	91.6

Performance by hardware and tool

Every hardware/tool/quantization combination unsloth/Qwen3.8-27B-GGUF:Q4_K_M has been benchmarked on, ranked by peak token generation speed. Last updated August 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
NVIDIA GeForce RTX 4090	llama.cpp	Q4_K_M	99.3 tok/s	95.3 tok/s	n/a	131,072 tokens	85.1	6

Benchmark runs

All 6 unsloth/Qwen3.8-27B-GGUF:Q4_K_M runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is unsloth/Qwen3.8-27B-GGUF:Q4_K_M good for coding?: In our benchmarks, unsloth/Qwen3.8-27B-GGUF:Q4_K_M scores 78.1/100 for coding. It runs at about 95.3 tok/s, so if you want more speed, Qwen3.6-35B-A3B-Q4_K_XL is faster (~156.9 tok/s) and still scores well for coding (82.2/100).
Is unsloth/Qwen3.8-27B-GGUF:Q4_K_M good for agentic (tool-using) tasks?: In our benchmarks, unsloth/Qwen3.8-27B-GGUF:Q4_K_M scores 85.9/100 for agentic workflows. It runs at about 95.3 tok/s, so if you want more speed, qwen3.8-flash-next-iq3_s is faster (~116.8 tok/s) and still scores well for agentic workflows (88.4/100).
How fast is unsloth/Qwen3.8-27B-GGUF:Q4_K_M for local inference?: Across 6 community benchmark runs, unsloth/Qwen3.8-27B-GGUF:Q4_K_M reaches up to 99.3 tok/s and averages 95.3 tok/s, with the fastest results on NVIDIA GeForce RTX 4090.
Which tools have been used to run unsloth/Qwen3.8-27B-GGUF:Q4_K_M?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.