Today's Local LLM Pick: nemotron-3-nano:30b on RTX 3090 (2026)

Fast verdict

nemotron-3-nano:30b is a moderate-speed general-purpose model on a 24GB RTX 3090 (57.0 tok/s). It is worth testing locally for batch or offline workloads. For real-time interactive use, measure end-to-end latency with your typical prompt length before committing.

nemotron-3-nano:30b approaches the 24GB boundary at higher quantizations. Consider Q4 or Q5 if you need context headroom on the RTX 3090. It ranks #8 of 18 in throughput among currently measured models on this RTX 3090. The next faster model is deepseek-r1:14b (74.4 tok/s, 30% faster). The next slower model is mistral-small:22b (54.8 tok/s, 4% slower).

The daily goal is simple: help a 3090 owner decide what to download tonight, what to skip, and when a cloud fallback is the better use of time.

Today’s pick

• Model: nemotron-3-nano:30b
• Category: general-purpose
• Size tier: large
• Performance tier: moderate
• RTX 3090 speed: 57.0 tok/s
• Latency: 2468 ms
• Test time: 2026-04-01T11:53:50Z
• Baseline command:

ollama run nemotron-3-nano:30b

Who should try it

• RTX 3090 owners deciding whether to download nemotron-3-nano:30b tonight for local experimentation.
• Users comparing local inference speed against cloud rental (RunPod, Vast) before committing to a workflow.
• Anyone building a local LLM toolbox who wants a verified baseline for this model.

Who should skip it

• Users who need long-context production stability before a sustained run has been verified.
• Teams whose workload requires predictable p95 latency under concurrency.
• 8GB/12GB GPU owners unless a smaller quantized variant exists.

Watch points

• Workload-specific testing: generic benchmarks do not guarantee performance on your particular use case.
• Context length: always test at your target context length before assuming production readiness.
• Quantization trade-off: lower quantization saves VRAM but may reduce output quality on nuanced tasks.

Verified benchmark anchors

• gpt-oss:20b: 156.1 tok/s | latency 1524 ms | test 2026-04-29T05:39:58Z
• qwen3-coder:30b: 140.5 tok/s | latency 935 ms | test 2026-06-17T07:31:11Z
• qwen3:8b: 121.7 tok/s | latency 1429 ms | test 2026-06-17T07:31:11Z
• qwen2.5-coder:32b: 92.2 tok/s | latency 1609 ms | test 2026-06-17T07:31:11Z
• qwen2.5:14b: 84.0 tok/s | latency 946 ms | test 2026-04-29T05:39:58Z

RTX 3090 decision guide

1. Batch is the sweet spot: nemotron-3-nano:30b is best for offline/batch jobs where throughput matters more than single-shot latency.
2. Test at your context length: moderate-speed models can slow significantly at longer contexts.
3. Quantization choice matters: stepping from Q8 to Q4 gains speed but test quality degradation first.
4. Cloud fallback plan: if local latency misses your target, use RunPod/Vast for time-sensitive runs.

Comparisons to validate

• nemotron-3-nano:30b vs the next-fastest and next-slowest model in the benchmark feed.
• nemotron-3-nano:30b vs gpt-oss:20b — same size tier, 57 vs 156 tok/s.
• nemotron-3-nano:30b local power cost vs A100 rental for the same workload.

Next actions

• Estimate VRAM fit: /en/tools/vram-calculator/
• Model page: /en/models/nemotron-3-nano-30b-q4/
• Benchmark changelog: /en/benchmarks/changelog/
• Local hardware path: /en/affiliate/hardware-upgrade/
• Cloud fallback: /go/runpod and /go/vast

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