Model benchmarks
Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp local LLM performance
As of September 2026, Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp runs at up to 40.0 tok/s for local inference (best of 2 community benchmark runs across 2 GPUs).
Model size
27B
Peak speed
40.0 tok/s
Average speed
26.6 tok/s
Avg PP
375.6 tok/s
Min memory
13.2 GB
Max context
131.072 tokens
Avg output / run
22.349 tokens
Avg runtime / run
16m 18s
Avg quality
83.5
Benchmark runs
2
GPUs tested
2
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.
| Task | P5 (low) | Avg | P95 (high) |
|---|---|---|---|
| Overall | 82.3 | 83.5 | 84.8 |
| Agent Workflow | 82.4 | 85.6 | 88.8 |
| Code Generation | 77.3 | 79.1 | 80.9 |
| Role Play & Narrative | 82.6 | 88.3 | 94.0 |
| Research & Analysis | 75.5 | 81.1 | 86.8 |
Performance by hardware and tool
Every hardware/tool/quantization combination Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp 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 5060 | llama.cpp | — | 40.0 tok/s | 40.0 tok/s | 13.2 GB | 131.072 tokens | 82.2 | 1 |
| NVIDIA GeForce RTX 3080 Ti | llama.cpp | — | 13.1 tok/s | 13.1 tok/s | 13.2 GB | 65.536 tokens | 84.9 | 1 |
Benchmark runs
All 2 Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp 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_XXS-mtp good for coding?
- In our benchmarks, Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp scores 79.1/100 for coding. It runs at about 26.6 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 Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp good for agentic (tool-using) tasks?
- In our benchmarks, Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp scores 85.6/100 for agentic workflows. It runs at about 26.6 tok/s, so if you want more speed, muse-glimmer:latest is faster (~34.5 tok/s) and still scores well for agentic workflows (93.0/100).
- How fast is Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp for local inference?
- Across 2 community benchmark runs, Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp reaches up to 40.0 tok/s and averages 26.6 tok/s, with the fastest results on NVIDIA GeForce RTX 5060.
- How much memory does Qwen3.8-27B-GSQ-RCO-IQ3_XXS-mtp 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_XXS-mtp?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.