Model benchmarks
qwen3.8-27b-gsq-rco@iq2_xs local LLM performance
As of October 2026, qwen3.8-27b-gsq-rco@iq2_xs runs at up to 33.9 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
33.9 tok/s
Average speed
31.2 tok/s
Avg PP
421.0 tok/s
Min memory
12.5 GB
Max context
65,536 tokens
Avg output / run
26,263 tokens
Avg runtime / run
17m 6s
Avg quality
75.6
Benchmark runs
3
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 3 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 | 66.9 | 75.6 | 85.9 |
| Agent Workflow | 41.8 | 64.6 | 85.3 |
| Code Generation | 69.1 | 78.0 | 85.3 |
| Role Play & Narrative | 77.0 | 82.7 | 90.5 |
| Research & Analysis | 67.3 | 77.2 | 84.0 |
Performance by hardware and tool
Every hardware/tool/quantization combination qwen3.8-27b-gsq-rco@iq2_xs 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 7900 GRE | LM Studio | IQ2_XS | 33.9 tok/s | 31.2 tok/s | 12.5 GB | 65,536 tokens | 75.6 | 3 |
Benchmark runs
All 3 qwen3.8-27b-gsq-rco@iq2_xs runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is qwen3.8-27b-gsq-rco@iq2_xs good for coding?
- In our benchmarks, qwen3.8-27b-gsq-rco@iq2_xs scores 78.0/100 for coding. It runs at about 31.2 tok/s, so if you want more speed, Qwen3.8-27B-oQ4e-fp16-mtp is faster (~38.6 tok/s) and still scores well for coding (82.6/100).
- Is qwen3.8-27b-gsq-rco@iq2_xs good for agentic (tool-using) tasks?
- In our benchmarks, qwen3.8-27b-gsq-rco@iq2_xs scores 64.6/100 for agentic workflows. It runs at about 31.2 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@iq2_xs for local inference?
- Across 3 community benchmark runs, qwen3.8-27b-gsq-rco@iq2_xs reaches up to 33.9 tok/s and averages 31.2 tok/s, with the fastest results on AMD Radeon RX 7900 GRE.
- How much memory does qwen3.8-27b-gsq-rco@iq2_xs need?
- The leanest observed configuration used about 12.5 GB of memory (quantizations tested: IQ2_XS).
- Which tools have been used to run qwen3.8-27b-gsq-rco@iq2_xs?
- Benchmarks were submitted using LM Studio. Results are community-contributed and updated as new runs arrive.