Model benchmarks
qwen3.8-27b-UD-Q4_K_XL local LLM performance
As of September 2026, qwen3.8-27b-UD-Q4_K_XL runs at up to 73.3 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
73.3 tok/s
Average speed
50.7 tok/s
Avg prefill
493.8 tok/s
Min memory
13.2 GB
Max context
131,072 tokens
Avg output / run
14,481 tokens
Avg runtime / run
5m 15s
Avg quality
84.9
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 | 82.5 | 84.9 | 86.4 |
| Agent Workflow | 81.9 | 83.7 | 86.0 |
| Code Generation | 77.1 | 80.6 | 84.8 |
| Role Play & Narrative | 78.5 | 86.9 | 93.6 |
| Research & Analysis | 88.1 | 88.5 | 89.0 |
Performance by hardware and tool
Every hardware/tool/quantization combination qwen3.8-27b-UD-Q4_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 |
|---|---|---|---|---|---|---|---|---|
| AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M | llama.cpp | Q4_K | 73.3 tok/s | 50.7 tok/s | 13.2 GB | 131,072 tokens | 84.9 | 3 |
Frequently asked questions
- Is qwen3.8-27b-UD-Q4_K_XL good for coding?
- In our benchmarks, qwen3.8-27b-UD-Q4_K_XL scores 80.6/100 for coding. It runs at about 50.7 tok/s, so if you want more speed, unsloth/Qwen3.8-27B-GGUF:IQ3_S is faster (~104.3 tok/s) and still scores well for coding (83.0/100).
- Is qwen3.8-27b-UD-Q4_K_XL good for agentic (tool-using) tasks?
- In our benchmarks, qwen3.8-27b-UD-Q4_K_XL scores 83.7/100 for agentic workflows. It runs at about 50.7 tok/s, so if you want more speed, Tiel-Coder-35B-A3B-Q4_K_S is faster (~165.0 tok/s) and still scores well for agentic workflows (89.3/100).
- How fast is qwen3.8-27b-UD-Q4_K_XL for local inference?
- Across 3 community benchmark runs, qwen3.8-27b-UD-Q4_K_XL reaches up to 73.3 tok/s and averages 50.7 tok/s, with the fastest results on AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M.
- How much memory does qwen3.8-27b-UD-Q4_K_XL need?
- The leanest observed configuration used about 13.2 GB of memory (quantizations tested: Q4_K).
- Which tools have been used to run qwen3.8-27b-UD-Q4_K_XL?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.