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

In our benchmarks, Qwen3.8-27B-GSQ-RCO-IQ3_S scores 72.0/100 for coding. It runs at about 57.2 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-GSQ-RCO-IQ3_S good for agentic (tool-using) tasks?

In our benchmarks, Qwen3.8-27B-GSQ-RCO-IQ3_S scores 85.6/100 for agentic workflows. It runs at about 57.2 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-GSQ-RCO-IQ3_S for local inference?

Across 9 community benchmark runs, Qwen3.8-27B-GSQ-RCO-IQ3_S reaches up to 74.9 tok/s and averages 57.2 tok/s, with the fastest results on NVIDIA GeForce RTX 4080 SUPER.

How much memory does Qwen3.8-27B-GSQ-RCO-IQ3_S need?

The leanest observed configuration used about 24.6 GB of memory.

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

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

Model benchmarks

Qwen3.8-27B-GSQ-RCO-IQ3_S local LLM performance

As of October 2026, Qwen3.8-27B-GSQ-RCO-IQ3_S runs at up to 74.9 tok/s for local inference (best of 9 community benchmark runs across 3 GPUs).

llama.cpp

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Model size

27B

Peak speed

74.9 tok/s

Average speed

57.2 tok/s

Avg PP

774.9 tok/s

Min memory

24.6 GB

Max context

188,416 tokens

Avg output / run

40,398 tokens

Avg runtime / run

13m 56s

Avg quality

83.5

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 9 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	74.6	83.5	87.8
Agent Workflow	79.2	85.6	91.0
Code Generation	40.3	72.0	82.9
Role Play & Narrative	84.2	90.5	95.1
Research & Analysis	83.4	86.1	90.3

Performance by hardware and tool

Every hardware/tool/quantization combination Qwen3.8-27B-GSQ-RCO-IQ3_S has been benchmarked on, ranked by peak token generation speed. Last updated October 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
NVIDIA GeForce RTX 4080 SUPER	llama.cpp	—	74.9 tok/s	73.6 tok/s	n/a	65,536 tokens	83.8	4
NVIDIA GeForce RTX 5070 Ti	llama.cpp	—	49.5 tok/s	48.6 tok/s	n/a	188,416 tokens	82.3	4
NVIDIA GeForce RTX 5060	llama.cpp	—	25.6 tok/s	25.6 tok/s	24.6 GB	131,072 tokens	87.6	1

Benchmark runs

All 9 Qwen3.8-27B-GSQ-RCO-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-GSQ-RCO-IQ3_S good for coding?: In our benchmarks, Qwen3.8-27B-GSQ-RCO-IQ3_S scores 72.0/100 for coding. It runs at about 57.2 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-GSQ-RCO-IQ3_S good for agentic (tool-using) tasks?: In our benchmarks, Qwen3.8-27B-GSQ-RCO-IQ3_S scores 85.6/100 for agentic workflows. It runs at about 57.2 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-GSQ-RCO-IQ3_S for local inference?: Across 9 community benchmark runs, Qwen3.8-27B-GSQ-RCO-IQ3_S reaches up to 74.9 tok/s and averages 57.2 tok/s, with the fastest results on NVIDIA GeForce RTX 4080 SUPER.
How much memory does Qwen3.8-27B-GSQ-RCO-IQ3_S need?: The leanest observed configuration used about 24.6 GB of memory.
Which tools have been used to run Qwen3.8-27B-GSQ-RCO-IQ3_S?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.