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Can I run Nemotron Nano 9B v2 on 8GB RAM Laptop (CPU/iGPU only)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. Nemotron Nano 9B v2 needs ~7.6 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.

Needs ~7.6 GB Device usable ~5 GB

Needs ~7.6 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 2.6 GB: Nemotron Nano 9B v2 needs roughly 7.6 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 Nano 9B v2 is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Nemotron Nano 9B v2 on Nvidia GeForce RTX 3060 (12GB).

Q4_K_M needed
~7.6 GB
Usable on device
~5 GB
Device memory
8 GB
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Which quant fits

Quant ladder vs ~5 GB usable
Q2_K
~5.3 GB
Q3_K_M
~5.9 GB
Q4_K_M
~7.6 GB
Q5_K_M
~7.9 GB
Q6_K
~8.9 GB
Q8_0
~10.3 GB
FP16
~19.3 GB
The line marks 8GB RAM Laptop (CPU/iGPU only)'s ~5 GB budget; rungs past it are too large.

How to run it

On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).

Model Nemotron
Parameters
9B
Q4_K_M size
6.08 GB
Q8_0 size
8.81 GB
Context
128k
Full Nemotron Nano 9B v2 requirements →
Device Windows
Memory
8 GB ram
Usable for weights
~5 GB
Power draw
~15 W
Best runtime
Ollama (llama.cpp backend)
Best models for 8GB RAM Laptop (CPU/iGPU only) →

What you can run instead

Run Nemotron Nano 9B v2 on other hardware

FAQ

Can 8GB RAM Laptop (CPU/iGPU only) run Nemotron Nano 9B v2?

No. Nemotron Nano 9B v2 needs ~7.6 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.

How much memory does Nemotron Nano 9B v2 need?

8GB RAM Laptop (CPU/iGPU only) does not have enough memory. At Q4_K_M the weights are ~6.08 GB; with KV cache and runtime overhead, budget ~7.6 GB at a 4k context.

What is the best tool to run Nemotron Nano 9B v2 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.

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Sources

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.