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Can the Apple M1 Max (32 GB) run FLUX.1 [schnell]?

68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (tight fit)

Runs well (tight fit). At FP8 it needs about 19.6 GB of 21.4 GB usable and takes about 16 s per image.

Best precision
FP8
Memory mode
Everything on GPU
Time per image
16 s
Output
1024×1024, 4 steps

Best app to run it on the Apple M1 Max (32 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 FLUX.1 [schnell] in Draw Things

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

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 FLUX.1 [schnell]

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/city96/FLUX.1-schnell-gguf (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Black Forest Labs, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/black-forest-labs/FLUX.1-schnell (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

Official repo · GitHub

Reference code, inference scripts and docs from Black Forest Labs. The weights themselves download from Hugging Face.

github.com/black-forest-labs/flux (opens in a new tab)

git clone https://github.com/black-forest-labs/flux

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

How it’s rated

Quality 68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 80/100 (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 Runs welltight fit
How well it runs on the Apple M1 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.30.9 / 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.23.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.11.8 / 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 score80.7 / 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 M1 Max (32 GB)

Quant Memory needed Available Speed Memory mode Runs Quality Download
FP16 11.0 GB 21.4 GB 23 s Sequential CPU offload Runs wellCPU offload 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)
FP8 19.6 GB 21.4 GB 16 s Everything on GPU Runs welltight fit 68Good quality (B) · 68 of 100: how capable the model is at FP8, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)
Q8 GGUF 20.4 GB 21.4 GB 21 s Everything on GPU Runs welltight fit 69Good quality (B) · 69 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4 GGUF 14.8 GB 21.4 GB 22 s Everything on GPU Runs well 65Good quality (B) · 65 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. GGUF (opens in a new tab)
NF4 14.4 GB 21.4 GB 20 s Everything on GPU Runs well 65Good quality (B) · 65 of 100: how capable the model is at NF4, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Other hardware that runs it

Quick answers

How much memory does FLUX.1 [schnell] need on the M1 Max 32GB?

At FP8 it needs about 19.6 GB. The M1 Max 32GB has 21.4 GB usable.

How long does FLUX.1 [schnell] take on the M1 Max 32GB?

About 16 s per image 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.

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