Model benchmarks
unsloth/Qwen3.8-27B-GGUF:UD-Q6_K_XL local LLM performance
As of August 2026, unsloth/Qwen3.8-27B-GGUF:UD-Q6_K_XL runs at up to 47.1 tok/s for local inference (best of 2 community benchmark runs across 2 GPUs).
llama.cppQ6_K
Model size
27B
Peak speed
47.1 tok/s
Average speed
36.1 tok/s
Min memory
13.2 GB
Max context
33,206 tokens
Best quality
94.8
Benchmark runs
2
GPUs tested
2
Performance by hardware and tool
Every hardware/tool/quantization combination unsloth/Qwen3.8-27B-GGUF:UD-Q6_K_XL has been benchmarked on, ranked by peak token generation speed. Last updated August 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 3080 Ti | llama.cpp | Q6_K | 47.1 tok/s | 47.1 tok/s | 13.2 GB | 33,206 tokens | 90.1 | 1 |
| Apple M5 Max | llama.cpp | Q6_K | 25.2 tok/s | 25.2 tok/s | 13.2 GB | 12,924 tokens | 85.5 | 1 |
Frequently asked questions
- How fast is unsloth/Qwen3.8-27B-GGUF:UD-Q6_K_XL for local inference?
- Across 2 community benchmark runs, unsloth/Qwen3.8-27B-GGUF:UD-Q6_K_XL reaches up to 47.1 tok/s and averages 36.1 tok/s, with the fastest results on NVIDIA GeForce RTX 3080 Ti.
- How much memory does unsloth/Qwen3.8-27B-GGUF:UD-Q6_K_XL need?
- The leanest observed configuration used about 13.2 GB of memory (quantizations tested: Q6_K).
- Which tools have been used to run unsloth/Qwen3.8-27B-GGUF:UD-Q6_K_XL?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.