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Can the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (128 GB) run Apodex 1.1 Mini (35B-A3B)?

81 ptsGreat (A) · 81 of 100 points: Model quality 39.6/45, Memory fit 25.0/25, Speed 7.8/15, Memory use 9.1/15

Yes, it runs well

Runs well. At Q6_K it needs about 37.9 GB of 96.0 GB usable, with an estimated speed of 11.0 tok/s.

Best quant
Q6_K
Generation speed
11.0 tok/s
Prompt processing
4,919 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Download Apodex 1.1 Mini (35B-A3B)

Quantized files · GGUF

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

huggingface.co/apodex/Apodex-1.1-mini-GGUF (opens in a new tab)

ollama run hf.co/apodex/Apodex-1.1-mini-GGUF

Original weights · Hugging Face

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

huggingface.co/apodex/Apodex-1.1-mini (opens in a new tab)

No sign-in needed.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the A grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.39.6 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.7.8 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.9.1 / 15
Total81.6 / 10081 ptsGreat (A) · 81 of 100 points: Model quality 39.6/45, Memory fit 25.0/25, Speed 7.8/15, Memory use 9.1/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (128 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 22.8 GB 96.0 GB 25.9 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 28.0/45, Memory fit 25.0/25, Speed 12.1/15, Memory use 5.5/15 GGUF files (opens in a new tab)
Q3_K_M 26.1 GB 96.0 GB 20.4 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 33.6/45, Memory fit 25.0/25, Speed 10.7/15, Memory use 6.3/15 GGUF files (opens in a new tab)
Q4_K_M 30.4 GB 96.0 GB 15.8 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 38.0/45, Memory fit 25.0/25, Speed 9.4/15, Memory use 7.3/15 GGUF (opens in a new tab)
Q5_K_M 34.0 GB 96.0 GB 13.0 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 38.8/45, Memory fit 25.0/25, Speed 8.6/15, Memory use 8.2/15 GGUF files (opens in a new tab)
Q6_K 37.9 GB 96.0 GB 11.0 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 39.6/45, Memory fit 25.0/25, Speed 7.8/15, Memory use 9.1/15 GGUF files (opens in a new tab)
Q8_0 46.6 GB 96.0 GB 8.0 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 40.1/45, Memory fit 25.0/25, Speed 6.7/15, Memory use 11.2/15 GGUF files (opens in a new tab)
F16 80.3 GB 96.0 GB 3.7 tok/s Runs well 84 ptsGreat (A) · 84 of 100 points: Model quality 40.1/45, Memory fit 25.0/25, Speed 4.6/15, Memory use 15.0/15 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

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.