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Can the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (64 GB) run Llama 4 Scout 17B-16E?

50Fair quality (C) · 50 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (over GPU memory limit)

Slow (over GPU memory limit). At Q2_K it needs about 49.5 GB of 48.0 GB usable, with an estimated speed of 7.6 tok/s.

Best quant
Q2_K
Generation speed
7.6 tok/s
Prompt processing
868 tok/s
First token (1K prompt)
1.2 s
Largest safe context
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Best app to run it on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (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 ggml-org/Llama-4-Scout-17B-16E-Instruct-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 ggml-org/Llama-4-Scout-17B-16E-Instruct-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 Llama 4 Scout 17B-16E

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 llama4:scout

Quantized files · GGUF

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

huggingface.co/ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF (opens in a new tab)

ollama run hf.co/ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF

Original weights · Hugging Face

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

huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Meta approves it (usually within minutes to a few hours).

Official repo · GitHub

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

github.com/meta-llama/llama-models (opens in a new tab)

Meta’s official Llama repo: model cards, prompt formats and download tools.

git clone https://github.com/meta-llama/llama-models

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

How it’s rated

Quality 50Fair quality (C) · 50 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 54/100 (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 Slowover GPU memory limit
How well it runs on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (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.22.7 / 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.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.6.5 / 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 score54.2 / 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 9 HX 370 (DDR5 SO-DIMM) (64 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 49.5 GB 48.0 GB 7.6 tok/s Slowover GPU memory limit 50Fair quality (C) · 50 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 59.3 GB 48.0 GB — Won't run 60Good quality (B) · 60 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 files (opens in a new tab)
Q4_K_M 72.3 GB 48.0 GB — Won't run 68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 2 (opens in a new tab)
Q5_K_M 83.2 GB 48.0 GB — Won't run 69Good quality (B) · 69 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q6_K 95.1 GB 48.0 GB — Won't run 71Great quality (A) · 71 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q8_0 121 GB 48.0 GB — Won't run 72Great quality (A) · 72 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.
F16 223 GB 48.0 GB — Won't run 72Great quality (A) · 72 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (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 4 Scout 17B-16E need on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 64GB?

At Q2_K it needs about 49.5 GB. The AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 64GB has 48.0 GB usable.

How fast is Llama 4 Scout 17B-16E on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 64GB?

About 7.6 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Llama 4 Scout 17B-16E should I use on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 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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