text model · Nemotron 3 · Windows
Can I run Nemotron 3 Nano 30B-A3B on 8GB RAM Laptop (CPU/iGPU only)?
No. Nemotron 3 Nano 30B-A3B needs ~25.1 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
Needs ~25.1 GB even at Q4_K_M, but only ~5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 20.1 GB: Nemotron 3 Nano 30B-A3B needs roughly 25.1 GB at Q4_K_M and 8GB RAM Laptop (CPU/iGPU only) leaves only about 5 GB usable for a model. The lightest tracked hardware that runs Nemotron 3 Nano 30B-A3B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Nemotron 3 Nano 30B-A3B on Nvidia GeForce RTX 5090 (32GB).
- Q4_K_M needed
- ~25.1 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
Which quant fits
- Parameters
- 30B (MoE, 3.5B active)
- Q4_K_M size
- 22.96 GB
- Q8_0 size
- 31.28 GB
- Context
- 256k
- Ollama tag
- nemotron-3-nano:30b-a3b
- Memory
- 8 GB ram
- Usable for weights
- ~5 GB
- Power draw
- ~15 W
- Best runtime
- Ollama (llama.cpp backend)
What you can run instead
Run Nemotron 3 Nano 30B-A3B on other hardware
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run Nemotron 3 Nano 30B-A3B?
No. Nemotron 3 Nano 30B-A3B needs ~25.1 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
How much memory does Nemotron 3 Nano 30B-A3B need?
8GB RAM Laptop (CPU/iGPU only) does not have enough memory. At Q4_K_M the weights are ~22.96 GB; with KV cache and runtime overhead, budget ~25.1 GB at a 4k context. It is a Mixture-of-Experts model (30B total / 3.5B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Nemotron 3 Nano 30B-A3B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/nemotron-3-nano-30b-a3b/laptop-8gb) Sources
- amazon.com
- en.wikipedia.org
- huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF
- huggingface.co/bartowski/nvidia_Nemotron-3-Nano-30B-A3B-GGUF
- huggingface.co/nvidia
- lmstudio.ai
- notebookcheck.net
- ollama.com
- ollama.com/library/llama3.2:1b
- ollama.com/library/nemotron-3-nano
- ollama.com/library/phi3:mini
- store.acer.com
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.