01What it can do
- High throughput thanks to 3B active parameters
- 128k context
- Reasoning and code
- ~17 GB VRAM in Q4
- —MoE 30B weights to load into memory
- —MoE tooling support varies by runtime
- —Gated weights on Hugging Face
04Install
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 3.5 Lightning 30B-A3B?
To run Nemotron 3.5 Lightning 30B-A3B locally with Q4 quantization, you need about 25 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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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.