Model benchmarks
Qwen3.8-27B-UD-Q6_K_XL local LLM performance
As of October 2026, Qwen3.8-27B-UD-Q6_K_XL runs at up to 90.1 tok/s for local inference (best of 6 community benchmark runs across 2 GPUs).
Model size
27B
Peak speed
90.1 tok/s
Average speed
64.1 tok/s
Avg PP
454.8 tok/s
Min memory
34.5 GB
Max context
262,144 tokens
Avg output / run
35,297 tokens
Avg runtime / run
9m 53s
Avg quality
81.1
Benchmark runs
6
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 6 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 | 67.1 | 81.1 | 88.0 |
| Agent Workflow | 51.0 | 78.1 | 89.4 |
| Code Generation | 64.9 | 78.1 | 85.2 |
| Role Play & Narrative | 75.5 | 86.3 | 92.2 |
| Research & Analysis | 74.0 | 82.1 | 86.1 |
Performance by hardware and tool
Every hardware/tool/quantization combination Qwen3.8-27B-UD-Q6_K_XL 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 |
|---|---|---|---|---|---|---|---|---|
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | vLLM | Q6_K | 90.1 tok/s | 84.3 tok/s | n/a | 250,000 tokens | 79.5 | 4 |
| NVIDIA RTX A6000 | llama.cpp | Q6_K | 23.9 tok/s | 23.8 tok/s | 34.5 GB | 262,144 tokens | 84.5 | 2 |
Benchmark runs
All 6 Qwen3.8-27B-UD-Q6_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-Q6_K_XL good for coding?
- In our benchmarks, Qwen3.8-27B-UD-Q6_K_XL scores 78.1/100 for coding. It runs at about 64.1 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-Q6_K_XL good for agentic (tool-using) tasks?
- In our benchmarks, Qwen3.8-27B-UD-Q6_K_XL scores 78.1/100 for agentic workflows. It runs at about 64.1 tok/s, so if you want more speed, Qwen3.8-Flash-Next-oQ3.5e-mtp is faster (~98.5 tok/s) and still scores well for agentic workflows (92.5/100).
- How fast is Qwen3.8-27B-UD-Q6_K_XL for local inference?
- Across 6 community benchmark runs, Qwen3.8-27B-UD-Q6_K_XL reaches up to 90.1 tok/s and averages 64.1 tok/s, with the fastest results on NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition.
- How much memory does Qwen3.8-27B-UD-Q6_K_XL need?
- The leanest observed configuration used about 34.5 GB of memory (quantizations tested: Q6_K).
- Which tools have been used to run Qwen3.8-27B-UD-Q6_K_XL?
- Benchmarks were submitted using llama.cpp, vLLM. Results are community-contributed and updated as new runs arrive.