Nemotron Cascade 2 30B-A3B
By NVIDIA · United States
Updated 2026-07-13
Overview
NVIDIA's 30B MoE (3B active) with both thinking and instruct modes. Earned IMO 2025 and IOI 2025 gold medals — 30B-class reasoning at 3B-active inference speed. Released April 2026.
When to pick this model
- Competition-grade math and code workloads
- Reasoning agents needing fast inference (3B active)
- Single-GPU deployments on 24 GB cards in Q4
- Production systems on NVIDIA Open Model License terms
- Tasks switching between thinking and instruct modes
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 Cascade 2 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 Cascade 2 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 Cascade 2 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 Cascade 2 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 GPU should you buy to run Nemotron Cascade 2 30B-A3B?
To run Nemotron Cascade 2 30B-A3B locally at Q4, you need ~17 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| AIME 2025 | 88 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- Gold medal at IMO 2025 and IOI 2025 in thinking mode
- Fast inference with only 3B active params
- Fits on a 24 GB GPU at Q4
- Commercial use allowed under NVIDIA Open Model License
Limitations
- NVIDIA Open Model License — not Apache or MIT
- 32+ GB VRAM total in Q4 (full model is 30B)
- Thinking mode generation can be slow
Typical workloads
In our catalog grid, Nemotron Cascade 2 30B-A3B is filed under Olympiad Math Reasoning, Competitive Coding, Agentic Agents — 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/3B active · unified thinking mode + instruct · 128k ctx
Training: Trained by NVIDIA. Gold medal at IMO 2025 and IOI 2025 in thinking mode. Optimized for mathematical reasoning and competitive code.
Olympic-grade reasoning at 3B-active inference cost — the sharpest open math and code model in its weight class.
Quick start
ollama run nemotron-cascade-2Or 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 Cascade 2 30B-A3B need?
At the recommended Q4_K_M quantization, Nemotron Cascade 2 30B-A3B needs about 17 GB of VRAM. Q8_0 takes 32 GB, and unquantized FP16 weights take 60 GB.
Can Nemotron Cascade 2 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 Cascade 2 30B-A3B support?
Nemotron Cascade 2 30B-A3B supports a 125k-token context window (128,000 tokens).
Can I use Nemotron Cascade 2 30B-A3B commercially?
Nemotron Cascade 2 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 Cascade 2 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 Cascade 2 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.
Is Nemotron Cascade 2 30B-A3B the right pick for you?