DreamShaper XL v2 Turbo on Apple M3 (8 GB)

Can the Apple M3 (8 GB) run DreamShaper XL v2 Turbo?

Yes, it runs well (tight fit · encoder unloaded after use)

Runs well (tight fit · encoder unloaded after use). At FP8 it needs about 4.7 GB of 5.4 GB usable and takes about 22 s per image.

Best precision: FP8
Memory mode: Text encoder offloaded
Time per image: 22 s
Output: 1024×1024, 8 steps

Best app to run it on the Apple M3 (8 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 DreamShaper XL v2 Turbo 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 DreamShaper XL v2 Turbo

Original weights · Hugging Face

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

huggingface.co/Lykon/dreamshaper-xl-v2-turbo (opens in a new tab)

No sign-in needed.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 56Good quality (B) · 56 of 100: how capable the model is at FP8, whatever it runs on. Match score 68/100 (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 · encoder unloaded after use: How well it runs on the Apple M3 (8 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.	25.6 / 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.	18.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	10.1 / 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 score	68.6 / 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 M3 (8 GB)

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	3.8 GB	5.4 GB	31 s	Sequential CPU offload	Runs wellCPU offload	58Good quality (B) · 58 of 100: how capable the model is at FP16, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	4.7 GB	5.4 GB	22 s	Text encoder offloaded	Runs welltight fit · encoder unloaded after use	56Good quality (B) · 56 of 100: how capable the model is at FP8, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Other hardware that runs it

58Good quality (B) · 58 of 100: how capable the model is at FP16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 FP16 · 5.4 s · Runs great
58Good quality (B) · 58 of 100: how capable the model is at FP16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB FP16 · 5.3 s · Runs great
58Good quality (B) · 58 of 100: how capable the model is at FP16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER FP16 · 2.4 s · Runs great
56Good quality (B) · 56 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. NVIDIA GeForce RTX 4060 FP8 · 4.2 s · Runs great
58Good quality (B) · 58 of 100: how capable the model is at FP16, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP16 · 1.7 s · Runs great
58Good quality (B) · 58 of 100: how capable the model is at FP16, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 Ti 16GB FP16 · 3.5 s · Runs great

Quick answers

How much memory does DreamShaper XL v2 Turbo need on the M3 8GB?

At FP8 it needs about 4.7 GB. The M3 8GB has 5.4 GB usable.

How long does DreamShaper XL v2 Turbo take on the M3 8GB?

About 22 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.