text model · Nemotron · Windows
Can I run Llama-3.3-Nemotron-Super-49B-v1 on AMD Ryzen AI Halo (128GB)?
Yes. Llama-3.3-Nemotron-Super-49B-v1 runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~30.6 GB of ~96 GB usable).
Runs at Q4_K_M using ~30.6 GB of ~96 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. AMD Ryzen AI Halo (128GB) leaves ~65.4 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~30.6 GB
- Usable on device
- ~96 GB
- Device memory
- 128 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~120 W
- Electricity / 1M tokens
- ~$0.83
At ~$0.15/kWh and the estimated ~6 tok/s, a million generated tokens costs about $0.83 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
- 128 GB unified
- Usable for weights
- ~96 GB
- Power draw
- ~120 W
- Best runtime
- llama.cpp (Vulkan/ROCm) / LM Studio
You could also run
Run Llama-3.3-Nemotron-Super-49B-v1 on other hardware
FAQ
Can AMD Ryzen AI Halo (128GB) run Llama-3.3-Nemotron-Super-49B-v1?
Yes. Llama-3.3-Nemotron-Super-49B-v1 runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~30.6 GB of ~96 GB usable).
How much memory does Llama-3.3-Nemotron-Super-49B-v1 need?
AMD Ryzen AI Halo (128GB) has room to spare. 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/nemotron-super-49b/amd-ryzen-ai-halo-128gb) 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.