Model benchmarks
Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed local LLM performance
As of October 2026, Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed runs at up to 50.6 tok/s for local inference (best of 5 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
50.6 tok/s
Average speed
47.7 tok/s
Avg PP
318.3 tok/s
Min memory
16.5 GB
Max context
128,000 tokens
Avg output / run
70,664 tokens
Avg runtime / run
24m 20s
Avg quality
86.4
Benchmark runs
5
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 5 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 | 83.6 | 86.4 | 89.6 |
| Agent Workflow | 85.7 | 87.6 | 89.9 |
| Code Generation | 72.6 | 81.0 | 89.0 |
| Role Play & Narrative | 82.8 | 88.4 | 92.1 |
| Research & Analysis | 85.8 | 88.5 | 91.6 |
Performance by hardware and tool
Every hardware/tool/quantization combination Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed 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 7800 XT | llama.cpp q8 q4 -1 budget | — | 50.6 tok/s | 50.6 tok/s | 16.9 GB | 128,000 tokens | 88.8 | 1 |
| AMD Radeon RX 7800 XT | llama.cpp q5 q5 -1 budget | — | 50.2 tok/s | 50.2 tok/s | 16.5 GB | 128,000 tokens | 84.4 | 1 |
| AMD Radeon RX 7800 XT | llama.cpp k8 v4 -1 budget | — | 49.8 tok/s | 49.4 tok/s | 16.9 GB | 128,000 tokens | 86.6 | 2 |
| AMD Radeon RX 7800 XT | llama.cpp k8 v8 -1 budget | — | 38.7 tok/s | 38.7 tok/s | 17.7 GB | 65,536 tokens | 85.5 | 1 |
Benchmark runs
All 5 Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed good for coding?
- In our benchmarks, Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed scores 81.0/100 for coding. It runs at about 47.7 tok/s, so if you want more speed, Nex-N2.5-mini-IQ3_XXS is faster (~91.4 tok/s) and still scores well for coding (81.6/100).
- Is Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed good for agentic (tool-using) tasks?
- In our benchmarks, Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed scores 87.6/100 for agentic workflows. It runs at about 47.7 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-IQ3_S-mtp-ASCII-Condensed for local inference?
- Across 5 community benchmark runs, Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed reaches up to 50.6 tok/s and averages 47.7 tok/s, with the fastest results on AMD Radeon RX 7800 XT.
- How much memory does Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed need?
- The leanest observed configuration used about 16.5 GB of memory.
- Which tools have been used to run Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp-ASCII-Condensed?
- Benchmarks were submitted using llama.cpp k8 v4 -1 budget, llama.cpp k8 v8 -1 budget, llama.cpp q5 q5 -1 budget, llama.cpp q8 q4 -1 budget. Results are community-contributed and updated as new runs arrive.