Nemotron Nano 3 30B-A3B
By NVIDIA · United States
Updated 2026-07-13
Overview
NVIDIA's Mamba-2 + Transformer hybrid MoE with 3B active out of 30B total parameters. A native 1M-token context with roughly 4× the throughput of Nemotron 2.
When to pick this model
- Million-token context workloads
- Edge and on-device inference at unusually long context
- Throughput-critical pipelines (RAG ingestion, log analysis)
- Hybrid SSM-Transformer research and benchmarking
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 19 GB |
| Q5_K_M | 23 GB |
| Q8_0 | 35 GB |
| FP16 (no quantization) | 62 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 Nano 3 30B-A3B wants a 24 GB card at Q4_K_M (19 GB). Stepping up to Q8_0 nearly doubles the footprint to 35 GB, and unquantized FP16 weights take 62 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Nemotron Nano 3 30B-A3B needs roughly 32 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 15 tokens/sec on entry-level GPUs, on the order of 40 tokens/sec on a mid-range card, and up to 100 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Nemotron Nano 3 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 Nano 3 30B-A3B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 19 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 19 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 19 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (23 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (23 GB used) |
Which GPU should you buy to run Nemotron Nano 3 30B-A3B?
To run Nemotron Nano 3 30B-A3B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Strengths
- Native 1M-token context window
- Ultra-efficient MoE with only 3B active parameters
- Roughly 4× throughput improvement over Nemotron 2
- Permissive NVIDIA Open Model license
Limitations
- Full 1M context consumes substantial VRAM in practice
- Hybrid architecture has thinner tooling support
- Distilled from Llama — inherits some base-model quirks
Typical workloads
In our catalog grid, Nemotron Nano 3 30B-A3B is filed under Extreme Long Context, Edge reasoning — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks.
The 976k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. 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 / 3B active · Nemotron-Nano-3 · 1M native context
Training: NVIDIA — distilled from Llama, edge-optimized with 1 million token context.
The throughput-and-context champion for edge MoE deployments — built for workloads where 128k context isn't enough.
Quick start
ollama run nemotron3:30bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
Similar models worth comparing
Frequently asked questions
How much VRAM does Nemotron Nano 3 30B-A3B need?
At the recommended Q4_K_M quantization, Nemotron Nano 3 30B-A3B needs about 19 GB of VRAM. Q8_0 takes 35 GB, and unquantized FP16 weights take 62 GB.
Can Nemotron Nano 3 30B-A3B run without a GPU?
Yes — with roughly 32 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 Nano 3 30B-A3B support?
Nemotron Nano 3 30B-A3B supports a 976k-token context window (1,000,000 tokens).
Can I use Nemotron Nano 3 30B-A3B commercially?
Nemotron Nano 3 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 Nano 3 30B-A3B on consumer hardware?
Our compatibility engine estimates on the order of 40 tokens/sec on a mid-range GPU and up to 100 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Nemotron Nano 3 30B-A3B should I download first?
Start with Q4_K_M (19 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.