Datasheet for local AI49 models98 GPUsData read 2026-09-25
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Mac 24 GB for local AI

Which image and video models run on an Apple Silicon Mac with 24 GB of unified memory, which file to download for each, and how much of the memory they need.

Apple24 GB unified · ~19.6 GB for the GPU · 100–307 GB/s · Apple SiliconData 2026-09-25
Unified memory24 GB
For the GPU~19.6 GB
Older macOS~17.2 GB
Bandwidth100–307
ChipsM2, M3, M4, M4 Pro, M5, M5 Pro, M6
FP8 filesNo
BackendPyTorch MPS
Runs well27 of 49

Specs: support.apple.com

01What runs on it

ModelVerdictBest fileSizeNeeded
image models
FLUX.1 [dev]Runs wellQ8_012.7 GB15.0 GB
FLUX.1 [schnell]Runs wellQ8_012.7 GB15.0 GB
FLUX.1 Kontext [dev]Runs wellQ8_012.7 GB15.3 GB
FLUX.1 Krea [dev]Runs wellQ8_012.7 GB15.0 GB
FLUX.1 Fill [dev]Runs wellQ8_012.7 GB15.3 GB
FLUX.2 [dev]TightQ3_K_M16.0 GB19.3 GB
FLUX.2 [klein] 9BRuns wellQ8_010.0 GB12.3 GB
FLUX.2 [klein] 4BRuns well16-bit7.8 GB9.6 GB
Krea 2 (Turbo)Runs wellQ8_013.7 GB16.3 GB
Qwen-ImageRunsQ5_K_M14.9 GB17.7 GB
Qwen-Image-Edit (2511)RunsQ5_K_M15.0 GB18.0 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 4RunsQ5_17.3 GB17.0 GB
Boogu-Image (Turbo)Runs wellQ8_011.6 GB14.2 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)RunsQ6_K14.7 GB17.5 GB
HiDream-I1 (Dev)RunsQ6_K14.7 GB17.5 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 wellQ8_09.7 GB12.0 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 2.1RunsQ6_K15.7 GB19.0 GB
video models
Wan 2.1 T2V 14BRunsQ6_K12.5 GB16.8 GB
Wan 2.1 T2V 1.3BRuns well16-bit2.8 GB5.6 GB
Wan 2.1 I2V 14B 480PRunsQ6_K14.2 GB18.5 GB
Wan 2.1 I2V 14B 720PRunsQ5_K_M12.7 GB19.5 GB
Wan 2.1 VACE 14BRunsQ6_K14.5 GB19.3 GB
Wan 2.2 T2V A14BRunsQ6_K12.0 GB16.3 GB
Wan 2.2 I2V A14BRunsQ6_K12.0 GB16.3 GB
Wan 2.2 TI2V 5BRuns well16-bit10.0 GB13.8 GB
Wan 2.2 Animate 14BRunsQ5_K_M13.0 GB18.3 GB
Wan Animate 2 (14B)RunsQ6_K14.2 GB19.5 GB
Wan 2.2 S2V 14BRunsQ4_K_M13.9 GB18.7 GB
SCAIL-2 (character animation)RunsQ6_K14.2 GB19.5 GB
HunyuanVideo (13B, original)Runs wellQ8_014.0 GB18.3 GB
LTX-Video 13B (0.9.8)Runs wellQ8_014.0 GB18.3 GB
HunyuanVideo 1.5Runs wellQ8_09.0 GB13.3 GB
LTX-2 (19B)RunsQ5_K_M14.3 GB19.1 GB
LTX-2.3 (22B)RunsQ4_K_M14.3 GB19.1 GB
LTX-2.5 (22B)TightQ3_K_M11.5 GB16.3 GB
MiniMax H3 (33B)Offload onlyQ3_K_M15.6 GB21.4 GB
MiniMax H3 PrunedRunsQ4_K_M11.6 GB17.4 GB

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

02Good to know

A Mac with 24 GB shares that memory between the CPU and the GPU. On current macOS the GPU may use about 19.6 GB of it by default (older macOS versions: about 17.2 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 24 GB: M2, M3, M4, M4 Pro, M5, M5 Pro, M6 (memory bandwidth 100–307 GB/s — the higher, the faster). Everything about Macs and local AI →

03Measured and reported results

No measured results yet. Send yours.