Nemotron Cascade 2 30B-A3B

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

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.

Which hardware should you buy to run Nemotron Cascade 2 30B-A3B?

To run Nemotron Cascade 2 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.

Published benchmark scores

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.

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-cascade-2

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

or all the kits, for life — $49 →

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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.