Model benchmarks
Qwen3.8-27B-UD-Q3_K_XL local LLM performance
As of September 2026, Qwen3.8-27B-UD-Q3_K_XL runs at up to 37.1 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
37.1 tok/s
Average speed
27.4 tok/s
Avg PP
356.2 tok/s
Min memory
13.2 GB
Max context
84.992 tokens
Avg output / run
28.067 tokens
Avg runtime / run
20m 46s
Avg quality
65.2
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 | 33.8 | 65.2 | 84.8 |
| Agent Workflow | 58.7 | 77.3 | 88.6 |
| Code Generation | 6.7 | 48.1 | 76.0 |
| Role Play & Narrative | 8.4 | 58.8 | 91.1 |
| Research & Analysis | 60.8 | 76.6 | 88.0 |
Performance by hardware and tool
Every hardware/tool/quantization combination Qwen3.8-27B-UD-Q3_K_XL 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 | Q3_K | 37.1 tok/s | 36.3 tok/s | 13.2 GB | 84.992 tokens | 83.5 | 2 |
| CPU only | llama.cpp | Q3_K | 9.7 tok/s | 9.7 tok/s | 13.2 GB | 8.192 tokens | 28.5 | 1 |
Benchmark runs
All 3 Qwen3.8-27B-UD-Q3_K_XL runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is Qwen3.8-27B-UD-Q3_K_XL good for coding?
- In our benchmarks, Qwen3.8-27B-UD-Q3_K_XL scores 48.1/100 for coding. It runs at about 27.4 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-UD-Q3_K_XL good for agentic (tool-using) tasks?
- In our benchmarks, Qwen3.8-27B-UD-Q3_K_XL scores 77.3/100 for agentic workflows. It runs at about 27.4 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-UD-Q3_K_XL for local inference?
- Across 3 community benchmark runs, Qwen3.8-27B-UD-Q3_K_XL reaches up to 37.1 tok/s and averages 27.4 tok/s, with the fastest results on NVIDIA GeForce RTX 5060.
- How much memory does Qwen3.8-27B-UD-Q3_K_XL need?
- The leanest observed configuration used about 13.2 GB of memory (quantizations tested: Q3_K).
- Which tools have been used to run Qwen3.8-27B-UD-Q3_K_XL?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.