Model benchmarks
unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL local LLM performance
As of November 2026, unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL runs at up to 61.5 tok/s for local inference (best of 6 community benchmark runs across 2 GPUs).
Model size
27B
Peak speed
61.5 tok/s
Average speed
39.1 tok/s
Avg PP
394.7 tok/s
Min memory
31.2 GB
Max context
131,072 tokens
Avg output / run
33,670 tokens
Avg runtime / run
15m 12s
Avg quality
79.5
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 | 70.8 | 79.5 | 84.7 |
| Agent Workflow | 81.7 | 87.0 | 92.7 |
| Code Generation | 6.9 | 54.8 | 81.1 |
| Role Play & Narrative | 84.0 | 90.3 | 95.1 |
| Research & Analysis | 83.7 | 86.0 | 89.0 |
Performance by hardware and tool
Every hardware/tool/quantization combination unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL has been benchmarked on, ranked by peak token generation speed. Last updated November 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 3080 | unsloth | Q4_K | 61.5 tok/s | 61.5 tok/s | 31.2 GB | 131,000 tokens | 83.4 | 1 |
| Advanced Micro Devices, Inc. [AMD/ATI] Device 7551 | llama.cpp | Q4_K | 41.5 tok/s | 34.6 tok/s | n/a | 131,072 tokens | 78.7 | 5 |
Benchmark runs
All 6 unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL good for coding?
- In our benchmarks, unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL scores 54.8/100 for coding. It runs at about 39.1 tok/s, so if you want more speed, swift-1.5-27b is faster (~98.7 tok/s) and still scores well for coding (84.8/100).
- Is unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL good for agentic (tool-using) tasks?
- In our benchmarks, unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL scores 86.9/100 for agentic workflows. It runs at about 39.1 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).
- How fast is unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL for local inference?
- Across 6 community benchmark runs, unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL reaches up to 61.5 tok/s and averages 39.1 tok/s, with the fastest results on NVIDIA GeForce RTX 3080.
- How much memory does unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL need?
- The leanest observed configuration used about 31.2 GB of memory (quantizations tested: Q4_K).
- Which tools have been used to run unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL?
- Benchmarks were submitted using llama.cpp, unsloth. Results are community-contributed and updated as new runs arrive.