Model benchmarks
qwen38-27b-turbo local LLM performance
As of October 2026, qwen38-27b-turbo runs at up to 29.8 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
29.8 tok/s
Average speed
29.7 tok/s
Avg PP
312.6 tok/s
Min memory
22.4 GB
Max context
8,192 tokens
Avg output / run
7,526 tokens
Avg runtime / run
4m 45s
Avg quality
77.8
Benchmark runs
2
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 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 | 77.7 | 77.8 | 77.9 |
| Agent Workflow | 74.5 | 79.8 | 85.1 |
| Code Generation | 66.9 | 69.7 | 72.5 |
| Role Play & Narrative | 70.7 | 78.4 | 86.0 |
| Research & Analysis | 83.5 | 83.5 | 83.5 |
Performance by hardware and tool
Every hardware/tool/quantization combination qwen38-27b-turbo has been benchmarked on, ranked by peak token generation speed. Last updated October 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| NVIDIA RTX A2000 12GB | llama.cpp | — | 29.8 tok/s | 29.7 tok/s | 22.4 GB | 8,192 tokens | 77.8 | 2 |
Benchmark runs
All 2 qwen38-27b-turbo runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is qwen38-27b-turbo good for coding?
- In our benchmarks, qwen38-27b-turbo scores 69.7/100 for coding. It runs at about 29.7 tok/s, so if you want more speed, Qwen3.8-27B-oQ4e-fp16-mtp is faster (~38.6 tok/s) and still scores well for coding (82.6/100).
- Is qwen38-27b-turbo good for agentic (tool-using) tasks?
- In our benchmarks, qwen38-27b-turbo scores 79.8/100 for agentic workflows. It runs at about 29.7 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).
- How fast is qwen38-27b-turbo for local inference?
- Across 2 community benchmark runs, qwen38-27b-turbo reaches up to 29.8 tok/s and averages 29.7 tok/s, with the fastest results on NVIDIA RTX A2000 12GB.
- How much memory does qwen38-27b-turbo need?
- The leanest observed configuration used about 22.4 GB of memory.
- Which tools have been used to run qwen38-27b-turbo?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.