Model benchmarks
unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S local LLM performance
As of September 2026, unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S runs at up to 46.7 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
27B
Peak speed
46.7 tok/s
Average speed
46.7 tok/s
Avg PP
1358.9 tok/s
Min memory
12.1 GB
Max context
32.768 tokens
Avg output / run
42.778 tokens
Avg runtime / run
15m 30s
Avg quality
54.3
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 | 42.5 | 54.3 | 66.0 |
| Agent Workflow | 80.3 | 85.3 | 90.2 |
| Code Generation | 0.3 | 2.9 | 5.5 |
| Role Play & Narrative | 79.7 | 84.3 | 88.8 |
| Research & Analysis | 4.5 | 44.6 | 84.8 |
Performance by hardware and tool
Every hardware/tool/quantization combination unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S 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 | — | 46.7 tok/s | 46.7 tok/s | 13.2 GB | 8.192 tokens | 41.2 | 1 |
| NVIDIA GeForce RTX 4080 SUPER | Ollama | IQ3_S | 46.7 tok/s | 46.7 tok/s | 12.1 GB | 32.768 tokens | 67.3 | 1 |
Benchmark runs
All 2 unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is hf.co/unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S good for coding?
- In our benchmarks, hf.co/unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S scores 2.9/100 for coding. It runs at about 46.7 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 hf.co/unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S good for agentic (tool-using) tasks?
- In our benchmarks, hf.co/unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S scores 85.3/100 for agentic workflows. It runs at about 46.7 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).
- How fast is unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S for local inference?
- Across 2 community benchmark runs, unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S reaches up to 46.7 tok/s and averages 46.7 tok/s, with the fastest results on NVIDIA GeForce RTX 4080 SUPER.
- How much memory does unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S need?
- The leanest observed configuration used about 12.1 GB of memory (quantizations tested: IQ3_S).
- Which tools have been used to run unsloth/Qwen3.8-27B-GGUF:UD-IQ3_S?
- Benchmarks were submitted using Ollama, llama.cpp. Results are community-contributed and updated as new runs arrive.