Is Qwen3.8-27B-UD-IQ3_S good for coding?

In our benchmarks, Qwen3.8-27B-UD-IQ3_S scores 77.9/100 for coding. It runs at about 78.1 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 Qwen3.8-27B-UD-IQ3_S good for agentic (tool-using) tasks?

In our benchmarks, Qwen3.8-27B-UD-IQ3_S scores 81.5/100 for agentic workflows. It runs at about 78.1 tok/s, so if you want more speed, Ornith-1.5-35B-A3B-BigBang-oQ8e-mtp is faster (~102.7 tok/s) and still scores well for agentic workflows (91.1/100).

How fast is Qwen3.8-27B-UD-IQ3_S for local inference?

Across 49 community benchmark runs, Qwen3.8-27B-UD-IQ3_S reaches up to 83.4 tok/s and averages 78.1 tok/s, with the fastest results on NVIDIA GeForce RTX 4080 SUPER.

Which tools have been used to run Qwen3.8-27B-UD-IQ3_S?

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

Model benchmarks

Qwen3.8-27B-UD-IQ3_S local LLM performance

As of September 2026, Qwen3.8-27B-UD-IQ3_S runs at up to 83.4 tok/s for local inference (best of 49 community benchmark runs across 2 GPUs).

llama.cpp

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Model size

27B

Peak speed

83.4 tok/s

Average speed

78.1 tok/s

Avg PP

1224.7 tok/s

Min memory

n/a

Max context

100,000 tokens

Avg output / run

17,782 tokens

Avg runtime / run

3m 44s

Avg quality

83.2

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 49 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	79.9	83.2	86.4
Agent Workflow	68.7	81.5	91.2
Code Generation	68.6	77.9	85.6
Role Play & Narrative	77.0	87.8	93.3
Research & Analysis	81.5	85.7	89.1

Performance by hardware and tool

Every hardware/tool/quantization combination Qwen3.8-27B-UD-IQ3_S 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
NVIDIA GeForce RTX 4080 SUPER	llama.cpp	—	83.4 tok/s	78.8 tok/s	n/a	100,000 tokens	83.2	48
NVIDIA GeForce RTX 5060	llama.cpp	—	42.9 tok/s	42.9 tok/s	n/a	65,536 tokens	81.9	1

Benchmark runs

All 49 Qwen3.8-27B-UD-IQ3_S 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-UD-IQ3_S good for coding?: In our benchmarks, Qwen3.8-27B-UD-IQ3_S scores 77.9/100 for coding. It runs at about 78.1 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 Qwen3.8-27B-UD-IQ3_S good for agentic (tool-using) tasks?: In our benchmarks, Qwen3.8-27B-UD-IQ3_S scores 81.5/100 for agentic workflows. It runs at about 78.1 tok/s, so if you want more speed, Ornith-1.5-35B-A3B-BigBang-oQ8e-mtp is faster (~102.7 tok/s) and still scores well for agentic workflows (91.1/100).
How fast is Qwen3.8-27B-UD-IQ3_S for local inference?: Across 49 community benchmark runs, Qwen3.8-27B-UD-IQ3_S reaches up to 83.4 tok/s and averages 78.1 tok/s, with the fastest results on NVIDIA GeForce RTX 4080 SUPER.
Which tools have been used to run Qwen3.8-27B-UD-IQ3_S?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.