Nemotron 3 Nano 30B-A3B
NVIDIA's 30B-parameter MoE with only 3.5B active per token, delivering 30B-class quality at small-model speeds across chat, code, and reasoning. 128k context.
By NVIDIA · United States
Updated 2026-09-15
When to pick this model
- Throughput-sensitive serving where latency matters more than peak quality
- Local inference with partial CPU offload (around 39GB system RAM)
- Long-context reasoning and coding without paying dense-model compute
- Workloads that previously needed a dense 30B but were too slow
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 17 GB |
| Q5_K_M | 21 GB |
| Q8_0 | 32 GB |
| FP16 (no quantization) | 60 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Nemotron 3 Nano 30B-A3B wants a 24 GB card at Q4_K_M (17 GB). Stepping up to Q8_0 nearly doubles the footprint to 32 GB, and unquantized FP16 weights take 60 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Nemotron 3 Nano 30B-A3B needs roughly 39 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 8 tokens/sec on entry-level GPUs, on the order of 30 tokens/sec on a mid-range card, and up to 80 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Nemotron 3 Nano 30B-A3B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Nemotron 3 Nano 30B-A3B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 17 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 17 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 17 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (21 GB used) |
| 32 GB | RTX 5090 | Q8_0 (32 GB used) |
Which hardware should you buy to run Nemotron 3 Nano 30B-A3B?
To run Nemotron 3 Nano 30B-A3B locally at Q4, you need ~17 GB for Q4 weights alone. Hardware option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.
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Strengths
- MoE routing yields 3.5B-class latency with 30B-class capability
- 128k context for large documents and repos
- Strong across chat, code, and reasoning in one checkpoint
- Distillation plus RL alignment from the broader Nemotron family
Limitations
- Needs ~39GB system RAM when partially offloaded to CPU
- NVIDIA Open Model License — review commercial terms
- Gated on Hugging Face
Typical workloads
In our catalog grid, Nemotron 3 Nano 30B-A3B is filed under Reasoning, Agentic Coding, Fast MoE Inference — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multi-step reasoning and math-flavoured tasks.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the NVIDIA Open Model License license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: MoE · 30B total / 3.5B active · 128k context
Training: Nemotron 3 family, distillation and RL alignment focused on reasoning, code, and chat.
The fast lane of the Nemotron 3 family — pick it when you want 30B output quality but can't afford 30B latency.
Quick start
Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.
Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.
ollama run nemotron-3-nanoOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Nemotron 3 Nano 30B-A3B need?
At the recommended Q4_K_M quantization, Nemotron 3 Nano 30B-A3B needs about 17 GB of VRAM. Q8_0 takes 32 GB, and unquantized FP16 weights take 60 GB.
Can Nemotron 3 Nano 30B-A3B run without a GPU?
Yes — with roughly 39 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Nemotron 3 Nano 30B-A3B support?
Nemotron 3 Nano 30B-A3B supports a 125k-token context window (128,000 tokens).
Can I use Nemotron 3 Nano 30B-A3B commercially?
Nemotron 3 Nano 30B-A3B ships under the NVIDIA Open Model License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Nemotron 3 Nano 30B-A3B on consumer hardware?
Our compatibility engine estimates on the order of 30 tokens/sec on a mid-range GPU and up to 80 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Nemotron 3 Nano 30B-A3B should I download first?
Start with Q4_K_M (17 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.