Model benchmarks
qwen3:8b local LLM performance
As of September 2026, qwen3:8b runs at up to 59.5 tok/s for local inference (best of 2 community benchmark runs across 2 GPUs).
Model size
8.2B
Peak speed
59.5 tok/s
Average speed
41.9 tok/s
Avg PP
2386.2 tok/s
Min memory
3.9 GB
Max context
8.192 tokens
Avg output / run
14.844 tokens
Avg runtime / run
7m 38s
Avg quality
44.6
Benchmark runs
2
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 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 | 37.0 | 44.6 | 52.2 |
| Agent Workflow | 70.4 | 72.7 | 75.0 |
| Code Generation | 0.0 | 0.0 | 0.0 |
| Role Play & Narrative | 62.3 | 71.8 | 81.2 |
| Research & Analysis | 15.3 | 33.9 | 52.4 |
Performance by hardware and tool
Every hardware/tool/quantization combination qwen3:8b 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 3060 Ti | Ollama | Q4_K_M | 59.5 tok/s | 59.5 tok/s | 6.1 GB | 8.192 tokens | 53.0 | 1 |
| Apple M5 | LM Studio | — | 24.2 tok/s | 24.2 tok/s | 3.9 GB | 8.192 tokens | 36.1 | 1 |
Benchmark runs
All 2 qwen3:8b runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is qwen/qwen3-8b good for coding?
- In our benchmarks, qwen/qwen3-8b scores 0.0/100 for coding. It runs at about 41.9 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 qwen/qwen3-8b good for agentic (tool-using) tasks?
- In our benchmarks, qwen/qwen3-8b scores 72.7/100 for agentic workflows. It runs at about 41.9 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 qwen3:8b for local inference?
- Across 2 community benchmark runs, qwen3:8b reaches up to 59.5 tok/s and averages 41.9 tok/s, with the fastest results on NVIDIA GeForce RTX 3060 Ti.
- How much memory does qwen3:8b need?
- The leanest observed configuration used about 3.9 GB of memory (quantizations tested: Q4_K_M).
- Which tools have been used to run qwen3:8b?
- Benchmarks were submitted using LM Studio, Ollama. Results are community-contributed and updated as new runs arrive.