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Can the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (96 GB) run Nemotron 3.5 Lightning 30B-A3B?

68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q4_K_M it needs about 25.7 GB of 72.0 GB usable, with an estimated speed of 28.5 tok/s.

Best quant
Q4_K_M
Generation speed
28.5 tok/s
Prompt processing
4,919 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Best app to run it on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (96 GB)

On Windows

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

  • Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.
lemonade pull unsloth/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama One command with Ollama’s own build of this model.

On Linux

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

  • Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.
lemonade pull unsloth/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama One command with Ollama’s own build of this model.

Want an agent? Nemotron 3.5 Lightning 30B-A3B supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Nemotron 3.5 Lightning 30B-A3B

Recommended for the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (96 GB): Q4_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run nemotron-3.5-lightning:30b-a3b-q4_K_M

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q4_K_M file (opens in a new tab)

huggingface.co/unsloth/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_K_M

Original weights · Hugging Face

Full-precision files from NVIDIA, for fine-tuning, vLLM, Transformers.

huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from NVIDIA. The weights themselves download from Hugging Face.

github.com/NVIDIA-NeMo/Nemotron (opens in a new tab)

NVIDIA’s Nemotron repo with recipes for the whole family.

git clone https://github.com/NVIDIA-NeMo/Nemotron

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs well
How well it runs on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (96 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.30.8 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.12.7 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.8.2 / 15
Match score76.7 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (96 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 19.0 GB 72.0 GB 44.6 tok/s Runs great 50Fair quality (C) · 50 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (IQ2_M) (opens in a new tab)
Q3_K_M 21.9 GB 72.0 GB 36.0 tok/s Runs great 60Good quality (B) · 60 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q3_K_XL) (opens in a new tab)
Q4_K_M 25.7 GB 72.0 GB 28.5 tok/s Runs well 68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 28.8 GB 72.0 GB 23.9 tok/s Runs well 69Good quality (B) · 69 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 32.3 GB 72.0 GB 20.3 tok/s Runs well 71Great quality (A) · 71 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q6_K_XL) (opens in a new tab)
Q8_0 39.9 GB 72.0 GB 15.1 tok/s Runs well 72Great quality (A) · 72 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 69.6 GB 72.0 GB 7.2 tok/s Slowtight fit 72Great quality (A) · 72 of 100: how capable the model is at F16, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does Nemotron 3.5 Lightning 30B-A3B need on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 96GB?

At Q4_K_M it needs about 25.7 GB. The AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 96GB has 72.0 GB usable.

How fast is Nemotron 3.5 Lightning 30B-A3B on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 96GB?

About 28.5 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Nemotron 3.5 Lightning 30B-A3B should I use on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 96GB?

Q4_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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