Model benchmarks

hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S local LLM performance

As of September 2026, hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S runs at up to 29.9 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).

OllamaIQ3_S
ShareRedditX

Model size

26.9B

Peak speed

29.9 tok/s

Average speed

24.2 tok/s

Avg PP

199.3 tok/s

Min memory

21.0 GB

Max context

180.000 tokens

Avg output / run

66.528 tokens

Avg runtime / run

49m 3s

Avg quality

85.2

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)
Overall84.785.285.8
Agent Workflow87.689.190.6
Code Generation73.574.074.4
Role Play & Narrative90.092.194.1
Research & Analysis83.885.887.8

Performance by hardware and tool

Every hardware/tool/quantization combination hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.

HardwareToolQuantBest tok/sAvg tok/sMemoryContextQualityRuns
AMD Radeon RX 7900 XTXOllamaIQ3_S29.9 tok/s24.2 tok/s21.0 GB180.000 tokens85.22

Benchmark runs

All 2 hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Frequently asked questions

Is hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S good for coding?
In our benchmarks, hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S scores 74.0/100 for coding. It runs at about 24.2 tok/s, so if you want more speed, /home/polaris/models/Tiel-Coder-35B-A3B-MTP-UD-Q6_K_XL.gguf is faster (~35.6 tok/s) and still scores well for coding (84.9/100).
Is hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S good for agentic (tool-using) tasks?
In our benchmarks, hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S scores 89.1/100 for agentic workflows. It runs at about 24.2 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 hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S for local inference?
Across 2 community benchmark runs, hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S reaches up to 29.9 tok/s and averages 24.2 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.
How much memory does hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S need?
The leanest observed configuration used about 21.0 GB of memory (quantizations tested: IQ3_S).
Which tools have been used to run hf.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF:IQ3_S?
Benchmarks were submitted using Ollama. Results are community-contributed and updated as new runs arrive.