Open-Sora 2.0 (11B) on AMD Radeon AI PRO R9700

Can the AMD Radeon AI PRO R9700 run Open-Sora 2.0 (11B)?

Barely: it runs very slowly (CPU offload)

Very slow (CPU offload). At FP16 it needs about 19.3 GB of 32.0 GB usable and takes about 87.3 min per clip.

Best precision: FP16
Memory mode: Sequential CPU offload
Time per clip: 87.3 min
Output: 768×768, 129 frames, 50 steps

Download Open-Sora 2.0 (11B)

Original weights · Hugging Face

Full-precision files from HPC-AI Tech, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/hpcai-tech/Open-Sora-v2 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/hpcaitech/Open-Sora (opens in a new tab)

git clone https://github.com/hpcaitech/Open-Sora

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP16, 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 Very slowCPU offload: How well it runs on the AMD Radeon AI PRO R9700: 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.	31.5 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.	12.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	2.8 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	13.9 / 15
Match score	60.2 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every precision on the AMD Radeon AI PRO R9700

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	19.3 GB	32.0 GB	87.3 min	Sequential CPU offload	Very slowCPU offload	70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 36.6 min · Slow
70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H100 80GB SXM FP16 · 34.8 min · Slow
70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H800 80GB SXM FP16 · 34.8 min · Slow
70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H100 NVL FP16 · 37.8 min · Slow
70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GH200 Grace Hopper (96 GB GPU) FP16 · 34.8 min · Slow
70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Server Edition FP16 · 37.8 min · Slow

Quick answers

How much memory does Open-Sora 2.0 (11B) need on the AI PRO R9700 32GB?

At FP16 it needs about 19.3 GB. The AI PRO R9700 32GB has 32.0 GB usable.

How long does Open-Sora 2.0 (11B) take on the AI PRO R9700 32GB?

About 87.3 min per clip at FP16 and default settings, an estimate scaled from a reference timing by this hardware’s compute speed.

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.