Open-Sora 2.0 (11B) on Apple M5 Max (48 GB)

Can the Apple M5 Max (48 GB) run Open-Sora 2.0 (11B)?

Barely: it runs very slowly (tight fit · encoder unloaded after use)

Very slow (tight fit · encoder unloaded after use). At FP16 it needs about 34.1 GB of 36.0 GB usable and takes about 5.0 h per clip.

Best precision: FP16
Memory mode: Text encoder offloaded
Time per clip: 5.0 h
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 65/100 (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 slowtight fit · encoder unloaded after use: How well it runs on the Apple M5 Max (48 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.	31.5 / 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.	18.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	1.5 / 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 score	66.0 / 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 Apple M5 Max (48 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	34.1 GB	36.0 GB	5.0 h	Text encoder offloaded	Very slowtight fit · encoder unloaded after use	70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 65/100 (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 M5 Max 48GB?

At FP16 it needs about 34.1 GB. The M5 Max 48GB has 36.0 GB usable.

How long does Open-Sora 2.0 (11B) take on the M5 Max 48GB?

About 5.0 h 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.