Model benchmarks
nvidia/nemotron-3-nano-4b local LLM performance
As of September 2026, nvidia/nemotron-3-nano-4b runs at up to 71.0 tok/s for local inference (best of 2 community benchmark runs across 1 GPU).
Model size
4B
Peak speed
71.0 tok/s
Average speed
50.6 tok/s
Avg prefill
747.2 tok/s
Min memory
2.0 GB
Max context
8,192 tokens
Avg output / run
9,112 tokens
Avg runtime / run
3m 59s
Avg quality
52.3
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 | 51.1 | 52.3 | 53.5 |
| Agent Workflow | 50.1 | 60.9 | 71.7 |
| Code Generation | 10.2 | 16.8 | 23.3 |
| Role Play & Narrative | 65.1 | 72.4 | 79.6 |
| Research & Analysis | 53.8 | 59.3 | 64.7 |
Performance by hardware and tool
Every hardware/tool/quantization combination nvidia/nemotron-3-nano-4b 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 |
|---|---|---|---|---|---|---|---|---|
| CPU only | LM Studio | — | 71.0 tok/s | 71.0 tok/s | 2.6 GB | 8,192 tokens | 51.0 | 1 |
| Apple M5 | LM Studio | — | 30.3 tok/s | 30.3 tok/s | 2.0 GB | 8,192 tokens | 53.6 | 1 |
Frequently asked questions
- Is nvidia/nemotron-3-nano-4b good for coding?
- In our benchmarks, nvidia/nemotron-3-nano-4b scores 16.8/100 for coding. It runs at about 50.6 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 nvidia/nemotron-3-nano-4b good for agentic (tool-using) tasks?
- In our benchmarks, nvidia/nemotron-3-nano-4b scores 60.9/100 for agentic workflows. It runs at about 50.6 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 nvidia/nemotron-3-nano-4b for local inference?
- Across 2 community benchmark runs, nvidia/nemotron-3-nano-4b reaches up to 71.0 tok/s and averages 50.6 tok/s.
- How much memory does nvidia/nemotron-3-nano-4b need?
- The leanest observed configuration used about 2.0 GB of memory.
- Which tools have been used to run nvidia/nemotron-3-nano-4b?
- Benchmarks were submitted using LM Studio. Results are community-contributed and updated as new runs arrive.