text model · Nemotron · Windows
Can I run Llama-3.3-Nemotron-Super-49B-v1 on Nvidia GeForce RTX 5090 (32GB)?
Yes. Llama-3.3-Nemotron-Super-49B-v1 runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~30.6 GB of ~31 GB usable).
Fits at Q4_K_M (~30.6 GB of ~31 GB usable) but with little headroom. Close other apps; a smaller context frees a few hundred MB.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 5090 (32GB) leaves ~0.4 GB of headroom.
- Q4_K_M needed
- ~30.6 GB
- Usable on device
- ~31 GB
- Device memory
- 32 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~575 W
- Electricity / 1M tokens
- ~$0.58
At ~$0.15/kWh and the estimated ~41 tok/s, a million generated tokens costs about $0.58 in electricity. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 2 load the same Q4_K_M weights.
llama-cli -hf bartowski/nvidia_Llama-3_3-Nemotron-Super-49B-v1-GGUF:Q4_K_M lms get bartowski/nvidia_Llama-3_3-Nemotron-Super-49B-v1-GGUF How to run it
On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).
- Parameters
- 49B
- Q4_K_M size
- 28.14 GB
- Q8_0 size
- 49.36 GB
- Context
- 128k
- Memory
- 32 GB vram
- Usable for weights
- ~31 GB
- Power draw
- ~575 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run Llama-3.3-Nemotron-Super-49B-v1 on other hardware
FAQ
Can Nvidia GeForce RTX 5090 (32GB) run Llama-3.3-Nemotron-Super-49B-v1?
Yes. Llama-3.3-Nemotron-Super-49B-v1 runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~30.6 GB of ~31 GB usable).
How much memory does Llama-3.3-Nemotron-Super-49B-v1 need?
It is a tight fit on Nvidia GeForce RTX 5090 (32GB). At Q4_K_M the weights are ~28.14 GB; with KV cache and runtime overhead, budget ~30.6 GB at a 4k context.
What is the best tool to run Llama-3.3-Nemotron-Super-49B-v1 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-super-49b/nvidia-rtx-5090-32gb) 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.