01What it can do
- IMO 2025 and IOI 2025 gold medalist
- Fast inference (3B active in production)
- Fits on a 24 GB GPU in Q4
- NVIDIA Open Model License (commercial use permitted)
- —NVIDIA Open Model license (not Apache/MIT)
- —32+ GB in Q4 (total model 30B)
- —Thinking mode can be slow during generation
05Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for Nemotron Cascade 2 30B-A3B?
To run Nemotron Cascade 2 30B-A3B locally with Q4 quantization, you need about 17 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.
Why this choice? Our complete guide on GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →
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On the go: Nemotron Cascade 2 30B-A3B also runs on a RTX laptop PC (24 GB of VRAM) →
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.
04Public benchmarks
Scores reproduced from model cards or MMLU-Pro / community sources. Unit: % correct answers.