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Can the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (64 GB) run ShieldGemma 9B?

63Good quality (B) · 63 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly

Slow. At Q6_K it needs about 12.7 GB of 48.0 GB usable and makes one decision in about 465 ms.

Best quant
Q6_K
Time per decision
465 ms
Decisions per second
2.1/s
Input assumed
512 tokens
Returns
Probability or score

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)

  • Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.
lemonade pull QuantFactory/shieldgemma-9b-GGUF:Q6_K
  • 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 QuantFactory/shieldgemma-9b-GGUF:Q6_K
  • 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 ShieldGemma 9B

Recommended for the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (64 GB): Q6_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 shieldgemma:9b

Quantized files · GGUF

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

Download the Q6_K file (opens in a new tab)

huggingface.co/QuantFactory/shieldgemma-9b-GGUF (opens in a new tab)

ollama run hf.co/QuantFactory/shieldgemma-9b-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/google/shieldgemma-9b (opens in a new tab)

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

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

How it’s rated

Quality 63Good quality (B) · 63 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q6_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 Slow
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.

PrecisionEditorial precision tier from the model’s published benchmarks, scaled by the quantization or precision used.28.5 / 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
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.7.0 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.15.0 / 15
Match score75.5 / 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
Q4_K_M 10.8 GB 48.0 GB 422 ms Slow 60Good quality (B) · 60 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 11.7 GB 48.0 GB 443 ms Slow 62Good quality (B) · 62 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 12.7 GB 48.0 GB 465 ms Slow 63Good quality (B) · 63 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 14.9 GB 48.0 GB 516 ms Slow 64Good quality (B) · 64 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 23.6 GB 48.0 GB 740 ms Slow 64Good quality (B) · 64 of 100: how capable the model is at F16, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Times are for one request with a 512-token input and about 1 tokens written. Batching servers process many requests at once.

Other hardware that runs it

Quick answers

How much memory does ShieldGemma 9B need on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 64GB?

At Q6_K it needs about 12.7 GB. The AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 64GB has 48.0 GB usable.

How fast is ShieldGemma 9B on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 64GB?

It makes one decision in about 465 ms at Q6_K, an estimate from the hardware and the size of the model.

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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