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Can the AMD Ryzen AI Max 385 (64 GB) run Llama 3.1 Nemotron 70B Instruct?

55Good quality (B) · 55 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (tight fit)

Slow (tight fit). At Q3_K_M it needs about 41.7 GB of 48.0 GB usable, with an estimated speed of 5.0 tok/s.

Best quant
Q3_K_M
Generation speed
5.0 tok/s
Prompt processing
403 tok/s
First token (1K prompt)
2.5 s
Largest safe context
26K

Best app to run it on the AMD Ryzen AI Max 385 (64 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 lmstudio-community/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF:Q3_K_L
  • 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 lmstudio-community/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF:Q3_K_L
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama One command with Ollama’s own build of this model.

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 Llama 3.1 Nemotron 70B Instruct

Recommended for the AMD Ryzen AI Max 385 (64 GB): Q3_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:70b-instruct-q3_K_M

Quantized files · GGUF

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

Download the Q3_K_M (Q3_K_L) file (opens in a new tab)

huggingface.co/lmstudio-community/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF (opens in a new tab)

ollama run hf.co/lmstudio-community/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF:Q3_K_L

Original weights · Hugging Face

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

huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF (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 55Good quality (B) · 55 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_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 Slowtight fit
How well it runs on the AMD Ryzen AI Max 385 (64 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.24.9 / 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.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.5.3 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score69.2 / 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 Max 385 (64 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 35.3 GB 48.0 GB 6.3 tok/s Slow 46Fair quality (C) · 46 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking.
Q3_K_M 41.7 GB 48.0 GB 5.0 tok/s Slowtight fit 55Good quality (B) · 55 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. GGUF (Q3_K_L) (opens in a new tab)
Q4_K_M 50.2 GB 48.0 GB 3.3 tok/s Slowover GPU memory limit 62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 57.2 GB 48.0 GB 2.7 tok/s Very slowfar over GPU memory limit 64Good quality (B) · 64 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.
Q6_K 65.0 GB 48.0 GB — Won't run 65Good quality (B) · 65 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 2 (opens in a new tab)
Q8_0 81.9 GB 48.0 GB — Won't run 66Good quality (B) · 66 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 2 (opens in a new tab)
F16 148 GB 48.0 GB — Won't run 66Good quality (B) · 66 of 100: how capable the model is at F16, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. 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 Llama 3.1 Nemotron 70B Instruct need on the AMD Ryzen AI Max 385 64GB?

At Q3_K_M it needs about 41.7 GB. The AMD Ryzen AI Max 385 64GB has 48.0 GB usable.

How fast is Llama 3.1 Nemotron 70B Instruct on the AMD Ryzen AI Max 385 64GB?

About 5.0 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Llama 3.1 Nemotron 70B Instruct should I use on the AMD Ryzen AI Max 385 64GB?

Q3_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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