Model benchmarks
Qwen3.8-27B-YMQ-M-TI local LLM performance
As of October 2026, Qwen3.8-27B-YMQ-M-TI runs at up to 36.2 tok/s for local inference (best of 4 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
36.2 tok/s
Average speed
35.5 tok/s
Avg PP
513.8 tok/s
Min memory
17.3 GB
Max context
81,920 tokens
Avg output / run
17,269 tokens
Avg runtime / run
8m 6s
Avg quality
84.3
Benchmark runs
4
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 4 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 | 81.8 | 84.3 | 87.6 |
| Agent Workflow | 72.2 | 84.5 | 91.5 |
| Code Generation | 72.7 | 77.5 | 80.0 |
| Role Play & Narrative | 81.2 | 89.1 | 93.3 |
| Research & Analysis | 82.4 | 86.0 | 90.6 |
Performance by hardware and tool
Every hardware/tool/quantization combination Qwen3.8-27B-YMQ-M-TI 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 GeForce RTX 5060 | llama.cpp | — | 36.2 tok/s | 35.5 tok/s | 17.3 GB | 81,920 tokens | 84.3 | 4 |
Benchmark runs
All 4 Qwen3.8-27B-YMQ-M-TI runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is Qwen3.8-27B-YMQ-M-TI good for coding?
- In our benchmarks, Qwen3.8-27B-YMQ-M-TI scores 77.5/100 for coding. It runs at about 35.5 tok/s, so if you want more speed, Nex-N2.5-mini-IQ3_XXS is faster (~80.3 tok/s) and still scores well for coding (84.5/100).
- Is Qwen3.8-27B-YMQ-M-TI good for agentic (tool-using) tasks?
- In our benchmarks, Qwen3.8-27B-YMQ-M-TI scores 84.5/100 for agentic workflows. It runs at about 35.5 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 Qwen3.8-27B-YMQ-M-TI for local inference?
- Across 4 community benchmark runs, Qwen3.8-27B-YMQ-M-TI reaches up to 36.2 tok/s and averages 35.5 tok/s, with the fastest results on NVIDIA GeForce RTX 5060.
- How much memory does Qwen3.8-27B-YMQ-M-TI need?
- The leanest observed configuration used about 17.3 GB of memory.
- Which tools have been used to run Qwen3.8-27B-YMQ-M-TI?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.