Model benchmarks
qwen3.5:9b local LLM performance
As of August 2026, qwen3.5:9b runs at up to 101.4 tok/s for local inference (best of 12 community benchmark runs across 6 GPUs).
LM StudioOllamaQ4_K_M
Model size
9.7B
Peak speed
101.4 tok/s
Average speed
47.6 tok/s
Min memory
4.4 GB
Max context
65,536 tokens
Avg runtime / run
4m 9s
Avg quality
67.8
Benchmark runs
12
GPUs tested
6
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 12 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 | 60.4 | 67.8 | 73.8 |
| Agent Workflow | 53.1 | 70.4 | 81.5 |
| Code Generation | 42.7 | 55.0 | 63.1 |
| Role Play & Narrative | 63.9 | 75.9 | 85.0 |
| Research & Analysis | 59.7 | 69.9 | 78.4 |
Performance by hardware and tool
Every hardware/tool/quantization combination qwen3.5:9b has been benchmarked on, ranked by peak token generation speed. Last updated August 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| AMD Radeon RX 7900 XTX | LM Studio | — | 101.4 tok/s | 101.4 tok/s | 7.0 GB | 8,192 tokens | 70.0 | 1 |
| NVIDIA GeForce RTX 5070 | LM Studio | — | 78.6 tok/s | 78.5 tok/s | 6.1 GB | 65,536 tokens | 72.6 | 2 |
| NVIDIA GeForce RTX 4070 Ti | LM Studio | — | 72.6 tok/s | 72.6 tok/s | 4.4 GB | 16,384 tokens | 68.5 | 1 |
| AMD Radeon RX 7700 XT / 7800 XT | Ollama | Q4_K_M | 64.6 tok/s | 64.6 tok/s | 7.5 GB | 65,536 tokens | 70.7 | 1 |
| NVIDIA GeForce RTX 4070 Ti SUPER | LM Studio | — | 62.7 tok/s | 62.7 tok/s | 9.7 GB | 16,384 tokens | 72.9 | 1 |
| NVIDIA GeForce RTX 5080 | Ollama | Q4_K_M | 24.1 tok/s | 22.3 tok/s | 8.2 GB | 16,384 tokens | 61.6 | 2 |
| AMD Radeon RX 7900 XTX | Ollama | Q4_K_M | 19.8 tok/s | 17.1 tok/s | 8.6 GB | 65,536 tokens | 65.8 | 4 |
Frequently asked questions
- Is qwen3.5:9b good for coding?
- In our benchmarks, qwen3.5:9b scores 55.0/100 for coding. It runs at about 47.6 tok/s, so if you want more speed, Gemma4:E2B/QAT-MTP@131K is faster (~303.9 tok/s) and still scores well for coding (66.9/100).
- Is qwen3.5:9b good for agentic (tool-using) tasks?
- In our benchmarks, qwen3.5:9b scores 70.4/100 for agentic workflows. It runs at about 47.6 tok/s, so if you want more speed, Gemma4:E2B/QAT-MTP@131K is faster (~303.9 tok/s) and still scores well for agentic workflows (71.7/100).
- How fast is qwen3.5:9b for local inference?
- Across 12 community benchmark runs, qwen3.5:9b reaches up to 101.4 tok/s and averages 47.6 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.
- How much memory does qwen3.5:9b need?
- The leanest observed configuration used about 4.4 GB of memory (quantizations tested: Q4_K_M).
- Which tools have been used to run qwen3.5:9b?
- Benchmarks were submitted using LM Studio, Ollama. Results are community-contributed and updated as new runs arrive.