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Can the AMD Ryzen AI Max 385 (64 GB) run Mistral Large 2 (123B)?

54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (far over GPU memory limit)

Slow (far over GPU memory limit). At Q2_K it needs about 56.6 GB of 48.0 GB usable, with an estimated speed of 3.1 tok/s.

Best quant
Q2_K
Generation speed
3.1 tok/s
Prompt processing
231 tok/s
First token (1K prompt)
4.4 s
Largest safe context
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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)

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.
lemonade pull bartowski/Mistral-Large-Instruct-2407-GGUF:Q2_K
  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.
  • LM Studio Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

Lemonade

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

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.
lemonade pull bartowski/Mistral-Large-Instruct-2407-GGUF:Q2_K
  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.
  • LM Studio Splits the model between GPU memory and system RAM, which this setup needs.

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 Mistral Large 2 (123B)

Recommended for the AMD Ryzen AI Max 385 (64 GB): Q2_K. 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 mistral-large:123b-instruct-2407-q2_K

Quantized files · GGUF

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

Download the Q2_K file (opens in a new tab)

huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Mistral-Large-Instruct-2407-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/mistralai/Mistral-Large-Instruct-2407 (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

Official repo · GitHub

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

github.com/mistralai/mistral-inference (opens in a new tab)

Mistral AI’s official inference code.

git clone https://github.com/mistralai/mistral-inference

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

How it’s rated

Quality 54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, 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 Slowfar over GPU memory limit
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.24.6 / 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.4.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.4.2 / 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 score47.7 / 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 Max 385 (64 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 56.6 GB 48.0 GB 3.1 tok/s Slowfar over GPU memory limit 54Fair quality (C) · 54 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 67.7 GB 48.0 GB — Won't run 65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q4_K_M 82.4 GB 48.0 GB — Won't run 74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q5_K_M 94.7 GB 48.0 GB — Won't run 75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q6_K 108 GB 48.0 GB — Won't run 77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q8_0 138 GB 48.0 GB — Won't run 78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
F16 253 GB 48.0 GB — Won't run 78Great quality (A) · 78 of 100: how capable the model is at F16, whatever it runs on. Match score 10/100 (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 Mistral Large 2 (123B) need on the AMD Ryzen AI Max 385 64GB?

At Q2_K it needs about 56.6 GB. The AMD Ryzen AI Max 385 64GB has 48.0 GB usable.

How fast is Mistral Large 2 (123B) on the AMD Ryzen AI Max 385 64GB?

About 3.1 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Mistral Large 2 (123B) should I use on the AMD Ryzen AI Max 385 64GB?

Q2_K. 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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