Can the Apple M2 Max (32 GB) run Allegro (2.8B)?

No, it doesn’t fit

No. Allegro (2.8B) needs about 24.7 GB even at FP16; the Apple M2 Max (32 GB) has 21.4 GB usable.

Download Allegro (2.8B)

Original weights · Hugging Face

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

huggingface.co/rhymes-ai/Allegro (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/rhymes-ai/Allegro (opens in a new tab)

git clone https://github.com/rhymes-ai/Allegro

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 52Fair quality (C) · 52 of 100: how capable the model is at FP16, whatever it runs on. Match score 7/100 (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 Won't run: How well it runs on the Apple M2 Max (32 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.	7.0 / 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.	0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.0 / 15
Match score	7.0 / 100

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

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every precision on the Apple M2 Max (32 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	24.7 GB	21.4 GB	—	—	Won't run	52Fair quality (C) · 52 of 100: how capable the model is at FP16, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Hardware that can run it

52Fair quality (C) · 52 of 100: how capable the model is at FP16, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 14.7 min · Slow
52Fair quality (C) · 52 of 100: how capable the model is at FP16, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 FP16 · 17.6 min · Slow (tight fit · encoder in RAM)
52Fair quality (C) · 52 of 100: how capable the model is at FP16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. AMD Ryzen AI Max+ 395 (128 GB) FP16 · 38.4 min · Slow
52Fair quality (C) · 52 of 100: how capable the model is at FP16, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA L40S FP16 · 18.5 min · Slow
52Fair quality (C) · 52 of 100: how capable the model is at FP16, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX 6000 Ada Generation FP16 · 18.6 min · Slow
52Fair quality (C) · 52 of 100: how capable the model is at FP16, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA L40 FP16 · 18.7 min · Slow

Models that do run on the Apple M2 Max (32 GB)

50Fair quality (C) · 50 of 100: how capable the model is at FP16, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2B FP16 · 3.1 min
83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. Bernini-R A14B (video generation + editing) FP8 · 83.4 min
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. HY-OmniWeaving (8.3B, unified video) FP8 · 61.4 min
68Good quality (B) · 68 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 I2V 14B 480P Q4 GGUF · 2.2 h
86Excellent quality (A+) · 86 of 100: how capable the model is at FP8, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. LTX-2.5 (22B, video + audio) FP8 · 13.0 min

Quick answers

How much memory does Allegro (2.8B) need on the M2 Max 32GB?

Even at FP16 it needs about 24.7 GB; the M2 Max 32GB has 21.4 GB usable.

What can the M2 Max 32GB run instead?

LTX-Video 2B, Bernini-R A14B (video generation + editing) and HY-OmniWeaving (8.3B, unified video) run best on it; the page lists more, each with its speed.

What hardware can run Allegro (2.8B)?

NVIDIA RTX PRO 6000 Blackwell 96GB, RTX 5090 32GB and AMD Ryzen AI Max+ 395 128GB run it; the page lists more, each with its 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.