Is qwen3.8-27b-gsq-rco good for coding?

In our benchmarks, qwen3.8-27b-gsq-rco scores 67.7/100 for coding. It runs at about 30.1 tok/s, so if you want more speed, Qwen3.8-27B-Turbo-Brilliance-50X-RI-GSQ-RCO-IQ3_XXS-mtp is faster (~37.5 tok/s) and still scores well for coding (83.8/100).

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

In our benchmarks, qwen3.8-27b-gsq-rco scores 86.6/100 for agentic workflows. It runs at about 30.1 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).

How fast is qwen3.8-27b-gsq-rco for local inference?

Across 10 community benchmark runs, qwen3.8-27b-gsq-rco reaches up to 49.5 tok/s and averages 30.1 tok/s, with the fastest results on AMD Radeon RX 9060 XT.

How much memory does qwen3.8-27b-gsq-rco need?

The leanest observed configuration used about 11.3 GB of memory (quantizations tested: IQ3_S).

Which tools have been used to run qwen3.8-27b-gsq-rco?

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

Model benchmarks

qwen3.8-27b-gsq-rco local LLM performance

As of October 2026, qwen3.8-27b-gsq-rco runs at up to 49.5 tok/s for local inference (best of 10 community benchmark runs across 6 GPUs).

LM StudioOllamallama.cppIQ3_S

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Model size

27B

Peak speed

49.5 tok/s

Average speed

30.1 tok/s

Avg PP

502.3 tok/s

Min memory

11.3 GB

Max context

153,600 tokens

Avg output / run

28,467 tokens

Avg runtime / run

18m 14s

Avg quality

81.6

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 10 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	66.5	81.6	88.4
Agent Workflow	79.8	86.6	91.7
Code Generation	29.1	67.7	82.9
Role Play & Narrative	67.8	87.6	95.2
Research & Analysis	75.8	84.3	88.7

Performance by hardware and tool

Every hardware/tool/quantization combination qwen3.8-27b-gsq-rco 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
AMD Radeon RX 9060 XT	LM Studio	—	49.5 tok/s	49.5 tok/s	n/a	8,192 tokens	81.6	1
Apple M5 Max	LM Studio	IQ3_S	43.8 tok/s	43.8 tok/s	24.4 GB	131,072 tokens	84.6	1
NVIDIA GeForce RTX 5070 Ti	llama.cpp	—	37.3 tok/s	37.3 tok/s	n/a	153,600 tokens	84.3	1
AMD Radeon RX 9070/9070 XT/9070 GRE	LM Studio	—	31.7 tok/s	24.1 tok/s	n/a	128,000 tokens	88.3	2
Apple M5 Max	LM Studio	—	29.9 tok/s	29.9 tok/s	n/a	8,192 tokens	61.3	1
AMD Radeon RX 9060 XT	LM Studio	IQ3_S	25.2 tok/s	25.2 tok/s	15.2 GB	16,384 tokens	72.8	1
AMD Radeon RX 7800 XT	Ollama	IQ3_S	23.9 tok/s	23.9 tok/s	11.3 GB	8,192 tokens	84.2	2
AMD Radeon RX 9070 XT	llama.cpp	—	19.3 tok/s	19.3 tok/s	22.9 GB	96,256 tokens	86.0	1

Benchmark runs

All 10 qwen3.8-27b-gsq-rco 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 good for coding?: In our benchmarks, qwen3.8-27b-gsq-rco scores 67.7/100 for coding. It runs at about 30.1 tok/s, so if you want more speed, Qwen3.8-27B-Turbo-Brilliance-50X-RI-GSQ-RCO-IQ3_XXS-mtp is faster (~37.5 tok/s) and still scores well for coding (83.8/100).
Is qwen3.8-27b-gsq-rco good for agentic (tool-using) tasks?: In our benchmarks, qwen3.8-27b-gsq-rco scores 86.6/100 for agentic workflows. It runs at about 30.1 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).
How fast is qwen3.8-27b-gsq-rco for local inference?: Across 10 community benchmark runs, qwen3.8-27b-gsq-rco reaches up to 49.5 tok/s and averages 30.1 tok/s, with the fastest results on AMD Radeon RX 9060 XT.
How much memory does qwen3.8-27b-gsq-rco need?: The leanest observed configuration used about 11.3 GB of memory (quantizations tested: IQ3_S).
Which tools have been used to run qwen3.8-27b-gsq-rco?: Benchmarks were submitted using LM Studio, Ollama, llama.cpp. Results are community-contributed and updated as new runs arrive.