Model benchmarks
unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M local LLM performance
As of September 2026, unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M runs at up to 65.6 tok/s for local inference (best of 2 community benchmark runs across 2 GPUs).
Model size
27B
Peak speed
65.6 tok/s
Average speed
55.2 tok/s
Avg PP
547.9 tok/s
Min memory
n/a
Max context
65.536 tokens
Avg output / run
16.860 tokens
Avg runtime / run
5m 2s
Avg quality
64.2
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 | 48.4 | 64.2 | 80.0 |
| Agent Workflow | 21.4 | 54.1 | 86.8 |
| Code Generation | 3.7 | 36.8 | 69.9 |
| Role Play & Narrative | 79.4 | 83.8 | 88.2 |
| Research & Analysis | 80.3 | 82.1 | 84.0 |
Performance by hardware and tool
Every hardware/tool/quantization combination unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M 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 |
|---|---|---|---|---|---|---|---|---|
| Apple M5 Max | vLLM | Q4_K_M | 65.6 tok/s | 65.6 tok/s | n/a | 8.192 tokens | 46.6 | 1 |
| NVIDIA GeForce GTX 1070 with Max-Q Design | llama.cpp | Q4_K_M | 44.8 tok/s | 44.8 tok/s | 13.2 GB | 65.536 tokens | 81.8 | 1 |
Benchmark runs
All 2 unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M 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_M good for coding?
- In our benchmarks, unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M scores 36.8/100 for coding. It runs at about 55.2 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 unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M good for agentic (tool-using) tasks?
- In our benchmarks, unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M scores 54.1/100 for agentic workflows. It runs at about 55.2 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_M for local inference?
- Across 2 community benchmark runs, unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M reaches up to 65.6 tok/s and averages 55.2 tok/s, with the fastest results on Apple M5 Max.
- How much memory does unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M need?
- The leanest observed configuration used about n/a of memory (quantizations tested: Q4_K_M).
- Which tools have been used to run unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_M?
- Benchmarks were submitted using llama.cpp, vLLM. Results are community-contributed and updated as new runs arrive.