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Can the AMD Ryzen AI Max+ 395 NAS (128 GB) run PaddleOCR-VL 1.6 0.9B?

42Fair quality (C) · 42 of 100: how capable the model is at F16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At F16 it needs about 10.4 GB of 96.0 GB usable, with an estimated speed of 66.1 tok/s.

Best quant
F16
Generation speed
66.1 tok/s
Prompt processing
38,363 tok/s
First token (1K prompt)
0.3 s
Largest safe context
128K

Best app to run it on the AMD Ryzen AI Max+ 395 NAS (128 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Usually the first to support new model architectures.
llama-server -hf PaddlePaddle/PaddleOCR-VL-1.6-GGUF:F16

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download PaddleOCR-VL 1.6 0.9B

Quantized files · GGUF

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

huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6-GGUF (opens in a new tab)

ollama run hf.co/PaddlePaddle/PaddleOCR-VL-1.6-GGUF

Original weights · Hugging Face

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

huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/PaddlePaddle/PaddleOCR (opens in a new tab)

git clone https://github.com/PaddlePaddle/PaddleOCR

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

How it’s rated

Quality 42Fair quality (C) · 42 of 100: how capable the model is at F16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at F16, 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 Runs great
How well it runs on the AMD Ryzen AI Max+ 395 NAS (128 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.18.9 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.2.5 / 15
Match score61.4 / 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+ 395 NAS (128 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 8.8 GB 96.0 GB 463 tok/s Runs great 29Basic quality (D) · 29 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 8.9 GB 96.0 GB 364 tok/s Runs great 35Basic quality (D) · 35 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 9.0 GB 96.0 GB 282 tok/s Runs great 39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q5_K_M 9.1 GB 96.0 GB 233 tok/s Runs great 40Fair quality (C) · 40 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q6_K 9.2 GB 96.0 GB 196 tok/s Runs great 41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q8_0 9.4 GB 96.0 GB 143 tok/s Runs great 42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
F16 10.4 GB 96.0 GB 66.1 tok/s Runs great 42Fair quality (C) · 42 of 100: how capable the model is at F16, whatever it runs on. Match score 61/100 (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 PaddleOCR-VL 1.6 0.9B need on the AMD Ryzen AI Max+ 395 NAS 128GB?

At F16 it needs about 10.4 GB. The AMD Ryzen AI Max+ 395 NAS 128GB has 96.0 GB usable.

How fast is PaddleOCR-VL 1.6 0.9B on the AMD Ryzen AI Max+ 395 NAS 128GB?

About 66.1 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of PaddleOCR-VL 1.6 0.9B should I use on the AMD Ryzen AI Max+ 395 NAS 128GB?

F16. 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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