Model benchmarks
qwen3.6:27b local LLM performance
As of July 2026, qwen3.6:27b runs at up to 9.3 tok/s for local inference (best of 2 community benchmark runs across 2 GPUs).
OllamaQ4_K_M
Model size
27.8B
Peak speed
9.3 tok/s
Average speed
7.0 tok/s
Min memory
19.3 GB
Max context
5,922 tokens
Best quality
91.0
Benchmark runs
2
GPUs tested
2
Performance by hardware and tool
Every hardware/tool/quantization combination qwen3.6:27b has been benchmarked on, ranked by peak token generation speed. Last updated July 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| AMD Radeon RX 7900 XTX | Ollama | Q4_K_M | 9.3 tok/s | 9.3 tok/s | 19.3 GB | 5,922 tokens | 76.5 | 1 |
| Apple M5 Max | Ollama | Q4_K_M | 4.8 tok/s | 4.8 tok/s | 25.1 GB | 5,757 tokens | 83.7 | 1 |
Frequently asked questions
- How fast is qwen3.6:27b for local inference?
- Across 2 community benchmark runs, qwen3.6:27b reaches up to 9.3 tok/s and averages 7.0 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.
- How much memory does qwen3.6:27b need?
- The leanest observed configuration used about 19.3 GB of memory (quantizations tested: Q4_K_M).
- Which tools have been used to run qwen3.6:27b?
- Benchmarks were submitted using Ollama. Results are community-contributed and updated as new runs arrive.