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 41.2 tok/s, so if you want more speed, nvidia/Qwen3.8-27B-NVFP4 is faster (~63.3 tok/s) and still scores well for coding (82.7/100).

Is nvidia/nemotron-3-nano-4b good for agentic (tool-using) tasks?

In our benchmarks, nvidia/nemotron-3-nano-4b scores 59.1/100 for agentic workflows. It runs at about 41.2 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).

How fast is nvidia/nemotron-3-nano-4b for local inference?

Across 12 community benchmark runs, nvidia/nemotron-3-nano-4b reaches up to 84.5 tok/s and averages 41.2 tok/s, with the fastest results on NVIDIA GeForce RTX 3060 Laptop GPU.

How much memory does nvidia/nemotron-3-nano-4b need?

The leanest observed configuration used about 624 MB of memory (quantizations tested: Q4_K_M).

Which tools have been used to run nvidia/nemotron-3-nano-4b?

Benchmarks were submitted using Bionic, LM Studio. Results are community-contributed and updated as new runs arrive.

Model benchmarks

nvidia/nemotron-3-nano-4b local LLM performance

As of October 2026, nvidia/nemotron-3-nano-4b runs at up to 84.5 tok/s for local inference (best of 12 community benchmark runs across 4 GPUs).

BionicLM StudioQ4_K_M

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Model size

Peak speed

84.5 tok/s

Average speed

41.2 tok/s

Avg PP

598.4 tok/s

Min memory

624 MB

Max context

65,536 tokens

Avg output / run

8,621 tokens

Avg runtime / run

9m 20s

Avg quality

48.9

Benchmark runs

GPUs tested

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	38.1	48.9	56.3
Agent Workflow	36.9	59.1	80.4
Code Generation	8.0	16.8	29.0
Role Play & Narrative	49.3	65.7	79.2
Research & Analysis	33.7	54.0	68.6

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 October 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
NVIDIA GeForce RTX 3060 Laptop GPU	Bionic	—	84.5 tok/s	78.3 tok/s	5.5 GB	65,536 tokens	52.0	4
CPU only	LM Studio	—	71.0 tok/s	71.0 tok/s	n/a	8,192 tokens	51.0	1
Apple M5	LM Studio	—	30.3 tok/s	30.3 tok/s	n/a	8,192 tokens	53.6	1
NVIDIA GeForce RTX 2050	LM Studio	Q4_K_M	27.3 tok/s	21.9 tok/s	624 MB	16,384 tokens	49.4	3
NVIDIA GeForce GTX 1050	LM Studio	Q4_K_M	5.0 tok/s	4.9 tok/s	3.9 GB	16,384 tokens	41.9	3

Benchmark runs

All 12 nvidia/nemotron-3-nano-4b runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

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 41.2 tok/s, so if you want more speed, nvidia/Qwen3.8-27B-NVFP4 is faster (~63.3 tok/s) and still scores well for coding (82.7/100).
Is nvidia/nemotron-3-nano-4b good for agentic (tool-using) tasks?: In our benchmarks, nvidia/nemotron-3-nano-4b scores 59.1/100 for agentic workflows. It runs at about 41.2 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).
How fast is nvidia/nemotron-3-nano-4b for local inference?: Across 12 community benchmark runs, nvidia/nemotron-3-nano-4b reaches up to 84.5 tok/s and averages 41.2 tok/s, with the fastest results on NVIDIA GeForce RTX 3060 Laptop GPU.
How much memory does nvidia/nemotron-3-nano-4b need?: The leanest observed configuration used about 624 MB of memory (quantizations tested: Q4_K_M).
Which tools have been used to run nvidia/nemotron-3-nano-4b?: Benchmarks were submitted using Bionic, LM Studio. Results are community-contributed and updated as new runs arrive.