text model · Nemotron · Windows
Can I run Nemotron Nano 9B v2 on Nvidia GeForce GTX 1070 (8GB)?
No. Nemotron Nano 9B v2 needs ~7.6 GB even at Q4_K_M, but Nvidia GeForce GTX 1070 (8GB) only has ~7 GB usable.
Needs ~7.6 GB even at Q4_K_M, but only ~7 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 0.6 GB: Nemotron Nano 9B v2 needs roughly 7.6 GB at Q4_K_M and Nvidia GeForce GTX 1070 (8GB) leaves only about 7 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
- ~7 GB
- Device memory
- 8 GB
Which quant fits
How to run it
On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).
- Parameters
- 9B
- Q4_K_M size
- 6.08 GB
- Q8_0 size
- 8.81 GB
- Context
- 128k
- Memory
- 8 GB vram
- Usable for weights
- ~7 GB
- Power draw
- ~150 W
- Best runtime
- llama.cpp CUDA (Pascal, no tensor cores)
What you can run instead
Run Nemotron Nano 9B v2 on other hardware
FAQ
Can Nvidia GeForce GTX 1070 (8GB) run Nemotron Nano 9B v2?
No. Nemotron Nano 9B v2 needs ~7.6 GB even at Q4_K_M, but Nvidia GeForce GTX 1070 (8GB) only has ~7 GB usable.
How much memory does Nemotron Nano 9B v2 need?
Nvidia GeForce GTX 1070 (8GB) 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.
Embed this
[](https://localmodel.run/can-i-run/nemotron-nano-9b/nvidia-gtx-1070-8gb) 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.