Model benchmarks
Qwen3.8-27B-GSQ-RCO-IQ3_S local LLM performance
As of September 2026, Qwen3.8-27B-GSQ-RCO-IQ3_S runs at up to 74.9 tok/s for local inference (best of 4 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
74.9 tok/s
Average speed
73.6 tok/s
Avg prefill
959.6 tok/s
Min memory
13.2 GB
Max context
65,536 tokens
Avg output / run
17,516 tokens
Avg runtime / run
3m 55s
Avg quality
83.8
Benchmark runs
4
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 4 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 | 80.6 | 83.8 | 87.5 |
| Agent Workflow | 79.3 | 84.7 | 89.2 |
| Code Generation | 67.8 | 75.3 | 80.6 |
| Role Play & Narrative | 84.7 | 88.8 | 92.8 |
| Research & Analysis | 83.1 | 86.3 | 90.7 |
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 September 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 | 13.2 GB | 65,536 tokens | 83.8 | 4 |
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 75.3/100 for coding. It runs at about 73.6 tok/s, so if you want more speed, Qwen3.6-35B-A3B-IQ4_NL is faster (~165.9 tok/s) and still scores well for coding (68.7/100).
- 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 84.7/100 for agentic workflows. It runs at about 73.6 tok/s, so if you want more speed, unsloth/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:UD-Q4_K_XL is faster (~169.2 tok/s) and still scores well for agentic workflows (83.0/100).
- How fast is Qwen3.8-27B-GSQ-RCO-IQ3_S for local inference?
- Across 4 community benchmark runs, Qwen3.8-27B-GSQ-RCO-IQ3_S reaches up to 74.9 tok/s and averages 73.6 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 13.2 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.