Datasheet for local AI49 models98 GPUsData read 2026-09-25
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Mac 36 GB for local AI

Which image and video models run on an Apple Silicon Mac with 36 GB of unified memory, which file to download for each, and how much of the memory they need.

Apple36 GB unified · ~30.2 GB for the GPU · 150–614 GB/s · Apple SiliconData 2026-09-25
Unified memory36 GB
For the GPU~30.2 GB
Older macOS~29 GB
Bandwidth150–614
ChipsM3 Pro, M3 Max, M4 Max, M5 Max
FP8 filesNo
BackendPyTorch MPS
Runs well47 of 49

Specs: support.apple.com

01What runs on it

ModelVerdictBest fileSizeNeeded
image models
FLUX.1 [dev]Runs well16-bit23.8 GB26.1 GB
FLUX.1 [schnell]Runs well16-bit23.8 GB26.1 GB
FLUX.1 Kontext [dev]Runs well16-bit23.8 GB26.4 GB
FLUX.1 Krea [dev]Runs well16-bit23.8 GB26.1 GB
FLUX.1 Fill [dev]Runs well16-bit23.8 GB26.4 GB
FLUX.2 [dev]RunsQ5_K_M24.1 GB27.4 GB
FLUX.2 [klein] 9BRuns well16-bit18.2 GB20.5 GB
FLUX.2 [klein] 4BRuns well16-bit7.8 GB9.6 GB
Krea 2 (Turbo)Runs well16-bit26.3 GB28.9 GB
Qwen-ImageRuns wellQ8_021.8 GB24.6 GB
Qwen-Image-Edit (2511)Runs wellQ8_021.8 GB24.8 GB
Qwen-Image 2.1Runs well16-bit14.2 GB16.5 GB
Z-Image TurboRuns well16-bit12.3 GB14.3 GB
Z-Image (base)Runs well16-bit12.3 GB14.3 GB
Ideogram 4Runs wellQ8_010.1 GB22.6 GB
Boogu-Image (Turbo)Runs well16-bit20.6 GB23.2 GB
ERNIE-Image (Turbo)Runs well16-bit16.1 GB18.4 GB
HiDream-O1-ImageRuns well16-bit16.4 GB19.2 GB
Mage-Flow (Microsoft)Runs well16-bit8.2 GB10.0 GB
Lumina Image 2.0Runs well16-bit5.2 GB7.0 GB
HiDream-I1 (Full)Runs wellQ8_018.7 GB21.5 GB
HiDream-I1 (Dev)Runs wellQ8_018.7 GB21.5 GB
Stable Diffusion 3.5 LargeRuns well16-bit16.5 GB18.8 GB
Stable Diffusion 3.5 MediumRuns well16-bit5.1 GB6.9 GB
Chroma1-HDRuns well16-bit17.8 GB20.1 GB
SDXL 1.0Runs well16-bit6.9 GB7.1 GB
Illustrious XL / Pony (SDXL anime)Runs well16-bit6.9 GB7.1 GB
Stable Diffusion 1.5Runs well16-bit2.1 GB3.3 GB
HunyuanImage 2.1Runs wellQ8_019.8 GB23.1 GB
video models
Wan 2.1 T2V 14BRuns wellQ8_015.9 GB20.2 GB
Wan 2.1 T2V 1.3BRuns well16-bit2.8 GB5.6 GB
Wan 2.1 I2V 14B 480PRuns wellQ8_018.1 GB22.4 GB
Wan 2.1 I2V 14B 720PRuns wellQ8_018.1 GB24.9 GB
Wan 2.1 VACE 14BRuns wellQ8_018.7 GB23.5 GB
Wan 2.2 T2V A14BRuns wellQ8_015.4 GB19.7 GB
Wan 2.2 I2V A14BRuns wellQ8_015.4 GB19.7 GB
Wan 2.2 TI2V 5BRuns well16-bit10.0 GB13.8 GB
Wan 2.2 Animate 14BRuns wellQ8_018.7 GB24.0 GB
Wan Animate 2 (14B)Runs wellQ8_018.1 GB23.4 GB
Wan 2.2 S2V 14BRuns wellQ8_019.6 GB24.4 GB
SCAIL-2 (character animation)Runs wellQ8_018.1 GB23.4 GB
HunyuanVideo (13B, original)Runs well16-bit25.6 GB29.9 GB
LTX-Video 13B (0.9.8)Runs wellQ8_014.0 GB18.3 GB
HunyuanVideo 1.5Runs well16-bit16.7 GB21.0 GB
LTX-2 (19B)Runs wellQ8_020.4 GB25.2 GB
LTX-2.3 (22B)Runs wellQ8_022.8 GB27.6 GB
LTX-2.5 (22B)Runs wellQ8_023.6 GB28.4 GB
MiniMax H3 (33B)RunsQ5_K_M23.9 GB29.7 GB
MiniMax H3 PrunedRuns wellQ8_021.6 GB27.4 GB

Calculated from real file sizes plus working memory. How the numbers work.

02Good to know

A Mac with 36 GB shares that memory between the CPU and the GPU. On current macOS the GPU may use about 30.2 GB of it by default (older macOS versions: about 29 GB); that is the figure used here. ComfyUI can go past it, but macOS then starts compressing and swapping memory and everything slows down. ComfyUI runs on Apple GPUs through PyTorch's MPS backend. 16-bit and GGUF files work; FP8 and INT8 files do not save memory on a Mac, so they are skipped. Speed is the catch: even the fastest Macs are several times slower per image than a desktop RTX card. Chips sold with 36 GB: M3 Pro, M3 Max, M4 Max, M5 Max (memory bandwidth 150–614 GB/s — the higher, the faster). Everything about Macs and local AI →

03Measured and reported results

No measured results yet. Send yours.