LTX-2 (19B, video + audio) on Apple M5 Ultra (96 GB)

Can the Apple M5 Ultra (96 GB) run LTX-2 (19B, video + audio)?

Yes, but slowly

Slow. At FP8 it needs about 44.3 GB of 72.0 GB usable and takes about 20.5 min per clip.

Best precision: FP8
Memory mode: Everything on GPU
Time per clip: 20.5 min
Output: 1280×704, 121 frames, 40 steps

Best app to run it on the Apple M5 Ultra (96 GB)

On macOS

Draw Things

Desktop app · runs on Draw Things (Metal FlashAttention)

Native Mac app with its own fast Metal engine and quantized downloads.

Open LTX-2 (19B, video + audio) in Draw Things

ComfyUI Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.
SwarmUI

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download LTX-2 (19B, video + audio)

Quantized files · GGUF for ComfyUI

Smaller GGUF versions for ComfyUI (with the ComfyUI-GGUF node). No sign-in needed; the model’s license still applies.

huggingface.co/unsloth/LTX-2-GGUF (opens in a new tab)

Original weights · Hugging Face

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

huggingface.co/Lightricks/LTX-2 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/Lightricks/LTX-2 (opens in a new tab)

git clone https://github.com/Lightricks/LTX-2

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

How it’s rated

Quality 80Great quality (A) · 80 of 100: how capable the model is at FP8, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP8, 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 Slow: How well it runs on the Apple M5 Ultra (96 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.	36.2 / 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.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	5.7 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	14.2 / 15
Match score	81.1 / 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 Ultra (96 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	51.0 GB	72.0 GB	24.0 min	Text encoder offloaded	Slowencoder unloaded after use	82Great quality (A) · 82 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.	Original weights (opens in a new tab)
`FP8`	44.3 GB	72.0 GB	20.5 min	Everything on GPU	Slow	80Great quality (A) · 80 of 100: how capable the model is at FP8, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`Q8 GGUF`	45.6 GB	72.0 GB	26.2 min	Everything on GPU	Slow	81Great quality (A) · 81 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4 GGUF`	36.7 GB	72.0 GB	27.4 min	Everything on GPU	Slow	76Great quality (A) · 76 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)

Other hardware that runs it

82Great quality (A) · 82 of 100: how capable the model is at FP16, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 4.9 min · Runs well
80Great quality (A) · 80 of 100: how capable the model is at FP8, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. AMD Ryzen AI Max+ 395 (128 GB) FP8 · 11.5 min · Runs well
76Great quality (A) · 76 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q4 GGUF · 7.0 min · Runs well (encoder in RAM)
80Great quality (A) · 80 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 FP8 · 7.8 min · Runs well (CPU offload)
80Great quality (A) · 80 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. AMD Radeon RX 7900 XTX FP8 · 8.7 min · Runs well (CPU offload)
82Great quality (A) · 82 of 100: how capable the model is at FP16, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GH200 Grace Hopper (96 GB GPU) FP16 · 4.6 min · Runs well

Quick answers

How much memory does LTX-2 (19B, video + audio) need on the M5 Ultra 96GB?

At FP8 it needs about 44.3 GB. The M5 Ultra 96GB has 72.0 GB usable.

How long does LTX-2 (19B, video + audio) take on the M5 Ultra 96GB?

About 20.5 min per clip at FP8 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.