Model benchmarks
Qwen3.8-27B-UD-IQ3_XXS local LLM performance
As of September 2026, Qwen3.8-27B-UD-IQ3_XXS runs at up to 81.6 tok/s for local inference (best of 3 community benchmark runs across 2 GPUs).
Model size
27B
Peak speed
81.6 tok/s
Average speed
74.5 tok/s
Avg PP
684.4 tok/s
Min memory
13.2 GB
Max context
65.536 tokens
Avg output / run
21.184 tokens
Avg runtime / run
4m 49s
Avg quality
57.8
Benchmark runs
3
GPUs tested
2
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 3 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 | 41.8 | 57.8 | 74.8 |
| Agent Workflow | 49.0 | 60.0 | 68.3 |
| Code Generation | 0.0 | 22.2 | 59.9 |
| Role Play & Narrative | 92.5 | 92.8 | 93.2 |
| Research & Analysis | 25.6 | 56.2 | 78.5 |
Performance by hardware and tool
Every hardware/tool/quantization combination Qwen3.8-27B-UD-IQ3_XXS has been benchmarked on, ranked by peak token generation speed. Last updated September 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 4080 SUPER | llama.cpp | — | 81.6 tok/s | 81.6 tok/s | 13.2 GB | 65.536 tokens | 76.9 | 1 |
| NVIDIA GeForce RTX 5070 Ti | llama.cpp | — | 72.1 tok/s | 70.9 tok/s | 13.2 GB | 8.192 tokens | 48.3 | 2 |
Benchmark runs
All 3 Qwen3.8-27B-UD-IQ3_XXS runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is Qwen3.8-27B-UD-IQ3_XXS good for coding?
- In our benchmarks, Qwen3.8-27B-UD-IQ3_XXS scores 22.2/100 for coding. It runs at about 74.5 tok/s, so if you want more speed, unsloth/Qwen3.8-27B-GGUF:IQ3_S is faster (~104.3 tok/s) and still scores well for coding (83.0/100).
- Is Qwen3.8-27B-UD-IQ3_XXS good for agentic (tool-using) tasks?
- In our benchmarks, Qwen3.8-27B-UD-IQ3_XXS scores 60.0/100 for agentic workflows. It runs at about 74.5 tok/s, so if you want more speed, Tiel-Coder-35B-A3B-Q4_K_S is faster (~165.0 tok/s) and still scores well for agentic workflows (89.3/100).
- How fast is Qwen3.8-27B-UD-IQ3_XXS for local inference?
- Across 3 community benchmark runs, Qwen3.8-27B-UD-IQ3_XXS reaches up to 81.6 tok/s and averages 74.5 tok/s, with the fastest results on NVIDIA GeForce RTX 4080 SUPER.
- How much memory does Qwen3.8-27B-UD-IQ3_XXS need?
- The leanest observed configuration used about 13.2 GB of memory.
- Which tools have been used to run Qwen3.8-27B-UD-IQ3_XXS?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.