Model benchmarks
Qwen3.8-27B-Q4_K_XL local LLM performance
As of August 2026, Qwen3.8-27B-Q4_K_XL runs at up to 75.6 tok/s for local inference (best of 5 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
75.6 tok/s
Average speed
71.0 tok/s
Min memory
13.2 GB
Max context
65,536 tokens
Avg output / run
14,960 tokens
Avg runtime / run
3m 31s
Avg quality
84.3
Benchmark runs
5
GPUs tested
1
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 5 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 | 82.8 | 84.3 | 86.1 |
| Agent Workflow | 79.5 | 83.4 | 88.7 |
| Code Generation | 76.8 | 81.5 | 85.6 |
| Role Play & Narrative | 79.5 | 86.5 | 91.6 |
| Research & Analysis | 83.0 | 85.9 | 88.9 |
Performance by hardware and tool
Every hardware/tool/quantization combination Qwen3.8-27B-Q4_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 |
|---|---|---|---|---|---|---|---|---|
| AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M | llama.cpp | Q4_K | 75.6 tok/s | 71.0 tok/s | 13.2 GB | 65,536 tokens | 84.3 | 5 |
Frequently asked questions
- Is Qwen3.8-27B-Q4_K_XL good for coding?
- In our benchmarks, Qwen3.8-27B-Q4_K_XL scores 81.5/100 for coding. It runs at about 71.0 tok/s, so if you want more speed, Tiel-Coder-35B-A3B-MLX-oQ4e-MTP is faster (~123.6 tok/s) and still scores well for coding (76.5/100).
- Is Qwen3.8-27B-Q4_K_XL good for agentic (tool-using) tasks?
- In our benchmarks, Qwen3.8-27B-Q4_K_XL scores 83.4/100 for agentic workflows. It runs at about 71.0 tok/s, so if you want more speed, openai/gpt-oss-20b:2 is faster (~154.8 tok/s) and still scores well for agentic workflows (87.2/100).
- How fast is Qwen3.8-27B-Q4_K_XL for local inference?
- Across 5 community benchmark runs, Qwen3.8-27B-Q4_K_XL reaches up to 75.6 tok/s and averages 71.0 tok/s, with the fastest results on AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M.
- How much memory does Qwen3.8-27B-Q4_K_XL need?
- The leanest observed configuration used about 13.2 GB of memory (quantizations tested: Q4_K).
- Which tools have been used to run Qwen3.8-27B-Q4_K_XL?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.