Model benchmarks
unsloth/Qwen3.5-9B-GGUF local LLM performance
As of October 2026, unsloth/Qwen3.5-9B-GGUF runs at up to 13.8 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
9B
Peak speed
13.8 tok/s
Average speed
13.5 tok/s
Avg PP
204.3 tok/s
Min memory
17.2 GB
Max context
65,536 tokens
Avg output / run
12,039 tokens
Avg runtime / run
15m 19s
Avg quality
69.6
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 | 67.6 | 69.6 | 71.5 |
| Agent Workflow | 65.4 | 70.0 | 74.6 |
| Code Generation | 48.7 | 53.5 | 58.3 |
| Role Play & Narrative | 70.4 | 73.8 | 77.2 |
| Research & Analysis | 79.0 | 81.0 | 82.9 |
Performance by hardware and tool
Every hardware/tool/quantization combination unsloth/Qwen3.5-9B-GGUF 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 |
|---|---|---|---|---|---|---|---|---|
| Intel(R) Arc(TM) 140V GPU (16GB) | Unsloth Studio | UD-Q4_K_XL | 13.8 tok/s | 13.8 tok/s | 17.2 GB | 65,536 tokens | 71.8 | 1 |
| Intel(R) Arc(TM) 140V GPU (16GB) | llama.cpp | — | 13.3 tok/s | 13.3 tok/s | n/a | 16,384 tokens | 67.4 | 1 |
Benchmark runs
All 2 unsloth/Qwen3.5-9B-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is unsloth/Qwen3.5-9B-GGUF good for coding?
- In our benchmarks, unsloth/Qwen3.5-9B-GGUF scores 53.5/100 for coding. It runs at about 13.5 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 unsloth/Qwen3.5-9B-GGUF good for agentic (tool-using) tasks?
- In our benchmarks, unsloth/Qwen3.5-9B-GGUF scores 70.0/100 for agentic workflows. It runs at about 13.5 tok/s, so if you want more speed, muse-glimmer:latest is faster (~33.3 tok/s) and still scores well for agentic workflows (91.6/100).
- How fast is unsloth/Qwen3.5-9B-GGUF for local inference?
- Across 2 community benchmark runs, unsloth/Qwen3.5-9B-GGUF reaches up to 13.8 tok/s and averages 13.5 tok/s, with the fastest results on Intel(R) Arc(TM) 140V GPU (16GB).
- How much memory does unsloth/Qwen3.5-9B-GGUF need?
- The leanest observed configuration used about 17.2 GB of memory (quantizations tested: UD-Q4_K_XL).
- Which tools have been used to run unsloth/Qwen3.5-9B-GGUF?
- Benchmarks were submitted using Unsloth Studio, llama.cpp. Results are community-contributed and updated as new runs arrive.