Can the Mac 16 GB run FLUX.1 Kontext [dev]?
Not at full quality. The 16-bit and 8-bit files are too big for the Mac 16 GB; the largest that fits is Q6_K (9.8 GB).
Q6_K (9.8 GB) fits with only 0.3 GB to spare (about 12.4 GB of 12.7 GB). Quality: very close to the original.
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | flux1-kontext-dev-Q6_K.gguf Q6_K · QuantStack/FLUX.1-Kontext-dev-GGUF | models/unet | 9.8 GB | Download → |
| Text encoder | t5-v1_1-xxl-encoder-Q8_0.gguf T5-XXL GGUF Q8_0 · city96/t5-v1_1-xxl-encoder-gguf | models/text_encoders | 5.1 GB | Download → |
| Text encoder | clip_l.safetensors CLIP-L · comfyanonymous/flux_text_encoders | models/text_encoders | 0.2 GB | Download → |
| VAE | ae.safetensors FLUX.1 VAE (ae) · Comfy-Org/Lumina_Image_2.0_Repackaged | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 15.5 GB | |||
Alternatives: T5-XXL FP16 (9.8 GB). On a Mac, FP8 text encoders do not load, so a 16-bit one is listed - or a GGUF one, loaded with “CLIPLoader (GGUF)” from the ComfyUI-GGUF nodes. Sizes from Hugging Face, 2026-10-01.
Free ComfyUI workflow, tested on my RTX 5060 Ti: FP8 workflow (.json) · GGUF Q8_0 workflow (.json). Drag the file into ComfyUI. All workflows →
Shared memory: about 21.5 GB of your 16 GB. On this machine the model, the text encoder, the VAE and the operating system all use the same memory. That is about 5.5 GB more than there is. It still runs: while the model works, the system compresses or swaps the idle text encoder, so loading and changing the prompt get slower. A smaller file or text encoder avoids that. calculated
02Every FLUX.1 Kontext file on 12.7 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · black-forest-labs | 23.8 GB | 26.4 GB | 13.7 GB too big | the original weights | Hugging Face → |
| Q8_0 GGUF · QuantStack | 12.7 GB | 15.3 GB | 2.6 GB too big | practically identical to the original | Hugging Face → |
| Q6_K ← GGUF · QuantStack | 9.8 GB | 12.4 GB | fits · 0.3 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 8.4 GB | 11.0 GB | fits · 1.7 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 6.9 GB | 9.5 GB | fits · 3.2 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 5.4 GB | 8.0 GB | fits · 4.7 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 4.0 GB | 6.6 GB | fits · 6.1 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 1.8 GB of working memory for FLUX.1 Kontext and a 0.8 GB reserve. Sizes 2026-10-01. FP8 and INT8 files are left out on a Mac: Apple GPUs cannot compute FP8, so ComfyUI either fails to load them or converts them back to 16-bit, which saves no memory. Use a 16-bit or GGUF file.
03Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run FLUX.1 Kontext on a Mac 16 GB, send your time per image and peak VRAM and it will appear here, credited.
04With more VRAM
With 16 GB you could run FLUX.1 Kontext at 8-bit or better (Q8_0, 12.7 GB) with no offloading: for example on the RTX 5080 16 GB, RTX 5070 Ti 16 GB, RTX 5060 Ti 16 GB.
05About the Mac 16 GB
A Mac with 16 GB shares that memory between the CPU and the GPU. On current macOS the GPU may use about 12.7 GB of it by default (older macOS versions: about 11.5 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 16 GB: M1, M1 Pro, M2, M2 Pro, M3, M4, M5, M6 (memory bandwidth 68.25–200 GB/s - the higher, the faster). Everything about Macs and local AI →
16 GB unified · ~12.7 GB for the GPU · 68.25–200 GB/s · Apple Silicon. Everything that runs on it →
06Questions
How much VRAM does FLUX.1 Kontext need?
Around 6.6 GB with the smallest file (Q2_K) and 14.5 GB with an 8-bit file (FP8). The full 16-bit file needs about 26.4 GB.
Which FLUX.1 Kontext file should I download for the Mac 16 GB?
Q6_K (9.8 GB) from QuantStack/FLUX.1-Kontext-dev-GGUF. Load it with Unet Loader (GGUF) from the ComfyUI-GGUF node pack.
FP8 or Q8_0 for FLUX.1 Kontext on the Mac 16 GB?
Q8_0 (12.7 GB). The Mac 16 GB has no FP8 maths, so FP8 (11.9 GB) only saves memory and is a step further from the original.
How much do I need to download for FLUX.1 Kontext on the Mac 16 GB?
15.5 GB: flux1-kontext-dev-Q6_K.gguf, t5-v1_1-xxl-encoder-Q8_0.gguf, clip_l.safetensors, ae.safetensors.