How much VRAM does SD 3.5 Medium need?

SD 3.5 Medium needs approximately 6 GB VRAM at FP16 for the transformer weights alone. With the triple text encoder loaded (T5-XXL + CLIP-L + OpenCLIP-G, 5.5B combined), total VRAM climbs to 8-10 GB. To fit on a 6-8 GB GPU, offload the T5-XXL encoder to CPU RAM — this drops active VRAM to approximately 6 GB at minimal quality cost.

Can SD 3.5 Medium run on an 8GB GPU?

Yes. SD 3.5 Medium runs on 8 GB GPUs like the RTX 4060 or RTX 3060 8GB with T5-XXL encoder offloaded to CPU. The model itself (2B MMDiT-X) fits in approximately 5-6 GB. ComfyUI and Diffusers both support CPU offloading for the text encoder. Expect slightly slower prompt encoding (1-2 seconds) but normal image generation speed.

What is the difference between SD 3.5 Medium and SDXL?

SD 3.5 Medium uses an MMDiT-X architecture (2B params) versus SDXL's UNet (2.6B params). SD 3.5 Medium offers better text rendering in images and stronger prompt adherence due to its triple text encoder (T5-XXL + CLIP-L + OpenCLIP-G). SDXL has a vastly larger ecosystem: thousands of LoRAs, ControlNets, and community fine-tunes. SDXL is also ~1 GB lighter on VRAM if you skip T5-XXL offloading.

Is SD 3.5 Medium better than SDXL?

For base model quality, prompt adherence, and text rendering, SD 3.5 Medium is better. For ecosystem depth, LoRA variety, ControlNet support, and workflow flexibility, SDXL wins decisively. If you run a lot of custom checkpoints, LoRA stacks, or ControlNet workflows, SDXL is the stronger practical choice. If you want a clean base model with good text generation, SD 3.5 Medium is compelling.

Does SD 3.5 Medium support LoRAs and ControlNets?

SD 3.5 Medium has a small but growing LoRA ecosystem. The available ControlNet for SD 3.5 is a Canny edge model (trained for SD 3.5 Large, with partial compatibility for Medium). The ecosystem is much smaller than SDXL's — hundreds of LoRAs vs thousands. Community support has improved since its October 2024 release.

How does SD 3.5 Medium compare to SD 3.5 Large?

SD 3.5 Medium is 2B parameters; SD 3.5 Large is 8B parameters. Medium needs ~6 GB at FP16 vs Large's 16-18 GB full precision (or ~10 GB with FP8). Medium generates faster and runs on consumer GPUs without quantization. Large produces notably better detail and composition at the cost of needing a 16-24 GB GPU.

May 20, 2026stable-diffusion, sd-3-5, vram, gpu-requirements, image-generation, sdxl, mmdit, comfyui

SD 3.5 Medium VRAM Requirements — 8GB GPU Guide + SD 3.5 Medium vs SDXL

SD 3.5 Medium needs ~6 GB at FP16 and fits on any 8GB GPU. Full VRAM table (FP16/FP8/Q4), minimum GPU list, and SD 3.5 Medium vs SDXL comparison.

If you are searching for SD 3.5 Medium VRAM requirements or wondering how it compares to SDXL on your hardware, this is the complete reference.

Stable Diffusion 3.5 Medium is the consumer-friendly version of the SD 3.5 family — a 2B MMDiT-X model that runs on 8 GB GPUs without quantization. That is the headline: no GGUF, no FP8 juggling, no quant trade-offs. Load FP16 weights and generate.

Use our VRAM calculator to check SD 3.5 Medium against your specific GPU.

Quick answers

SD 3.5 Medium VRAM (FP16, T5 offloaded): ~6 GB — fits on 8 GB GPU
SD 3.5 Medium VRAM (FP16, full encoders): ~9-10 GB — needs 10-12 GB GPU
Minimum GPU: RTX 4060 8GB or RTX 3060 8GB (with CPU text encoder offload)
Comfortable GPU: RTX 4070 12GB or better
vs SDXL: better prompt adherence and text rendering; smaller ecosystem

SD 3.5 Medium model specs

Feature	SD 3.5 Medium
Architecture	MMDiT-X (transformer-based)
Transformer parameters	2.0 billion
Text encoders	T5-XXL (4.7B) + CLIP-L + OpenCLIP-G = 5.5B combined
VAE	80M parameters
Default steps	28
Supported resolution	512×512 to 1024×1024
Max resolution	1024×1024
Model file (FP16)	5.1 GB safetensors
License	Stability AI Community License
Provider	Stability AI
Release	October 22, 2024
HF repo	stabilityai/stable-diffusion-3.5-medium

Key architectural note: The 2B MMDiT-X weights are the model itself. The 5.5B text encoders are only active during prompt encoding (a single pass at the start of generation) and can be offloaded to CPU RAM after encoding — leaving only the 2B DiT in VRAM during denoising.

SD 3.5 Medium exact VRAM table

Precision	Weights in VRAM	+ Text encoders	Total VRAM	Notes
FP16 (T5 on CPU)	~5.1 GB	~0.8 GB (CLIP only)	~6 GB	Recommended for 8 GB GPUs
FP16 (all encoders)	~5.1 GB	~4.9 GB	~10 GB	All encoders in VRAM
FP16 + LoRA	~5.5 GB	~0.8 GB	~7.5 GB	LoRA adds ~300-500 MB
FP16 + ControlNet (Canny)	~6.5 GB	~0.8 GB	~8.5 GB	ControlNet for SD 3.5

All numbers are at 1024×1024. At 512×512, reduce by ~0.5 GB for activations. No significant GGUF/Q4 format is widely available for SD 3.5 Medium as of May 2026 (the model already fits without quantization).

Minimum GPU requirements

GPU	VRAM	SD 3.5 Medium	Notes
RTX 4060 8GB	8 GB	Yes (T5 offloaded)	Works well; enable CPU offload in ComfyUI
RTX 4060 Ti 8GB	8 GB	Yes (T5 offloaded)	Same as RTX 4060
RTX 3060 12GB	12 GB	Yes, comfortable	Full encoders in VRAM
RTX 4070 12GB	12 GB	Yes, comfortable	Full encoders with headroom
RTX 4060 Ti 16GB	16 GB	Easy	LoRAs + ControlNet stack
RTX 4070 Ti Super 16GB	16 GB	Easy	Fast generation
RTX 4080 Super 16GB	16 GB	Easy + fast	Best value 16GB
RTX 4090 24GB	24 GB	Overkill — consider SD 3.5 Large	See below

6 GB GPUs (GTX 1060, RTX 3060 6GB): SD 3.5 Medium does not fit without significant model sharding — use SDXL or SD 1.5 instead.

How to run SD 3.5 Medium on 8 GB

In ComfyUI, enable the --lowvram flag or set the FP16 model to use sequential CPU offloading. Specifically for SD 3.5 Medium:

Load sd3.5_medium.safetensors into the SD3 model loader
Use a T5 text encoder with the CPU offload node, or set ComfyUI to run the clip model on CPU
The VAE loads into VRAM only during decode (~500 MB); tile VAE if needed

In Diffusers (Python), use enable_model_cpu_offload() or enable_sequential_cpu_offload() on the pipeline. Prompt encoding runs on CPU (~2 seconds), then denoising runs on GPU.

SD 3.5 Medium vs SDXL — detailed comparison

This is the most common question for 8-12 GB GPU owners:

	SD 3.5 Medium	SDXL 1.0
Architecture	MMDiT-X (2B)	UNet (2.6B)
VRAM required	~6 GB (T5 offloaded)	~7.5 GB
Text rendering	Good — T5-XXL gives strong text	Poor — CLIP-only struggles
Prompt adherence	Strong	Moderate
Photorealism	Good	Good with fine-tunes
Generation speed (RTX 4070)	~10-14 sec / image	~5-8 sec / image
LoRAs available	Hundreds	5,000+
ControlNets	1 (Canny, partial)	5+ types
Fine-tuned checkpoints	Few	Hundreds
License	Stability Community	OpenRAIL++

Choose SD 3.5 Medium if:

You want better text in images (signs, labels, captions)
You want stronger prompt adherence out of the box
You do not need a large LoRA or ControlNet ecosystem
You run ComfyUI and want a clean MMDiT workflow

Choose SDXL if:

You rely on community fine-tunes (Pony Diffusion, Animagine, Juggernaut, RealVisXL)
You use ControlNet-heavy workflows
You need a large LoRA library
You prioritize speed (SDXL generates ~1.5-2x faster per image)
You want anime-specific quality (Pony, Illustrious XL)

SD 3.5 Medium vs SD 3.5 Large

If you can spare the VRAM, SD 3.5 Large is noticeably better:

	SD 3.5 Medium	SD 3.5 Large
Transformer params	2B	8B
VRAM FP16 (T5 offloaded)	~6 GB	~16-18 GB
VRAM FP8	—	~10-12 GB
Image detail	Good	Excellent
Text rendering	Good	Very good
Min GPU	RTX 4060 8GB	RTX 4080 16GB (FP8)

For 16-24 GB GPUs, SD 3.5 Large in FP8 (via ComfyUI) is worth the upgrade. See the Image Generation VRAM Guide for the full family breakdown.

Apple Silicon and SD 3.5 Medium

Apple Silicon Macs run SD 3.5 Medium well via the Diffusers library and mlx-diffusers. Unified memory means you do not need to offload the T5 encoder:

Mac	Unified RAM	SD 3.5 Medium	Notes
MacBook Air M4 16GB	16 GB	Excellent	Full encoders, LoRA stack
Mac M4 Pro 24GB	24 GB	Excellent + fast	Can run multiple models
Mac M4 Max 36GB	36 GB	SD 3.5 Large FP16 too	Consider upgrading
Any M3/M4 with 16GB+	16 GB+	No issues	—

Expect ~15-25 seconds per image on M4 MacBook Air 16GB at 1024×1024 — slower than NVIDIA but no VRAM management needed.

GPU recommendations by tier

8 GB tier — RTX 4060, RTX 3060 8GB

SD 3.5 Medium with T5 offloaded to CPU is your primary option at this tier. Enable ComfyUI --lowvram or sequential CPU offload. Expect occasional 1-2 second prompt encoding pauses. Generation speed after that is normal.

If you want faster generation speed and the broadest ecosystem, SDXL remains the better choice at 8 GB. If you prioritize text-in-image quality and prompt accuracy, SD 3.5 Medium is worth the setup overhead.

12 GB tier — RTX 4070, RTX 3060 12GB

12 GB is the sweet spot for SD 3.5 Medium. All three text encoders fit in VRAM (10 GB), leaving 2 GB for activations and a LoRA. No offloading needed. This is the tier where SD 3.5 Medium feels effortless.

Flux.1 Dev GGUF Q5-Q6 is also worth considering at 12 GB if you want the best available image quality — it beats both SD 3.5 Medium and SDXL on photorealism.

16 GB tier — RTX 4060 Ti 16GB, RTX 4080 Super 16GB

16 GB is more VRAM than SD 3.5 Medium needs. At this tier, consider:

SD 3.5 Medium with full LoRA + ControlNet stack
SD 3.5 Large FP16 (fits at 16-18 GB total with T5 on CPU)
Flux.1 Dev FP8 (~15 GB) for best-in-class image quality

24 GB tier — RTX 4090, RTX 3090

SD 3.5 Medium at 24 GB is overkill for the model itself. Use the headroom for:

Batch generation (multiple images simultaneously)
Running SD 3.5 Large FP16 with all encoders loaded
Flux.1 Dev FP8 with full ControlNet + LoRA stack

Related guides

Image Generation VRAM Guide 2026 — complete VRAM table for Flux, SDXL, SD 3.5
Flux vs SDXL vs SD 3.5 Comparison — head-to-head quality and ecosystem
Best Local Image Generation Models 2025 — ranked model guide
VRAM Calculator — diffusion models — check your GPU against any model
How to Run Flux Locally — if you want to step up to Flux