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Local image generation models: SDXL, Flux and how much VRAM

The main open image models for running at home, roughly how much graphics memory each needs, which tools to use, and how to try it in your browser.

Updated Oct 26, 2026 · RunItLocal.io

For image generation at home, SDXL is the easy, well-supported choice on 8 to 12 GB cards, and Flux gives better images and better text in pictures if you have 16 to 24 GB. Older Stable Diffusion 1.5 still runs on almost anything. To try it with no install at all, our Studio generates images in your browser on your own GPU.

The main model families

FamilyQualityRough VRAMNotes
Stable Diffusion 1.5Dated, but huge ecosystem~4 to 6 GBThousands of fine-tunes and add-ons
SDXLGood~8 to 12 GBThe well-supported middle ground
FluxVery good, strong at text in images~12 GB with quantized files, 24 GB+ at full sizeBig model, slower per image
Fast "turbo" modelsRough, but near instant~4 GB and upOne to a few steps per image

The VRAM figures are approximate. They depend on image size, the tool, and its memory-saving settings. Many tools can run bigger models on smaller cards by moving parts of the model to system memory, at a cost in speed. Quantized versions of Flux, including GGUF files, cut memory a lot for a small loss in quality. See quantization explained.

Images are different from text

Text generation is limited by memory bandwidth; see why bandwidth sets speed. Image generation runs the model many times per picture and leans much harder on raw compute. So:

  • Newer NVIDIA cards win clearly. Tensor cores matter here.
  • A card that's great for chat can be poor for images. Our rack's Tesla P100s are fast at text but slow at images, because they have no tensor cores. See the Tesla P100 for local LLMs.
  • Macs work, more slowly than a similar-priced NVIDIA card.
  • AMD and Intel cards work with some tools, with more setup. See AMD GPUs for local AI.

The RTX 3060 12 GB is a popular starter for images: enough memory for SDXL with room to spare. A used RTX 3090 runs Flux comfortably.

Try it in your browser first

Our Studio runs SD-Turbo entirely on your own GPU through WebGPU. Nothing is sent to a server. It's about a 2.5 GB download the first time, then it's cached and works offline. It makes 512×512 images in a single step, so it's fast but rough next to SDXL or Flux. It's a quick way to see if your machine handles image generation before you install anything. It needs a browser with WebGPU support.

The tools

  • ComfyUI builds image pipelines from connected nodes. It's the most flexible, and usually the first to support new models.
  • Stable Diffusion WebUI (AUTOMATIC1111) and its forks, like Forge, give you a classic form-style interface.
  • InvokeAI is a polished app with a canvas for editing.
  • Draw Things is a popular native app on Mac.

All of these are free and run locally. ComfyUI or a Forge-style WebUI is a good first pick on a PC.

Getting good results

  • Use the size a model was trained on. SDXL and Flux are built for around 1024×1024; SD 1.5 for 512×512. Far off that and you get stretched or duplicated subjects.
  • Steps and guidance matter. Each model has recommended settings on its model card; start there. See how to read a model card.
  • Fine-tunes and LoRAs add styles and subjects. They must match the base model family.
  • Seeds let you repeat an image and tweak one thing at a time.

Licenses vary more than for text models

Image models come under a wide range of licenses, and some popular ones are for non-commercial use only. Fine-tunes inherit the base model's terms. Check before you use images commercially. See open model licenses explained.

Images and chat on one card

A 24 GB card can hold a chat model or an image model comfortably, but not both large ones at once. Ollama unloads idle models after a while, or you can stop one before starting the other. Here's what a 3090 runs on the chat side:

RTX 3090, 24 GB · estimated with our calculator · full page

  • Qwen3 4BQuick everyday chat and summaries
    ~210 tok/s
    Runs greatqwen3:4b
  • Qwen3 8BA solid all-rounder for chat and writing
    ~99 tok/s
    Runs greatqwen3:8b
  • Gemma 3 12BReads images as well as text
    ~64 tok/s
    Runs greatgemma3:12b
  • Qwen3 14BNoticeably smarter at reasoning and code
    ~55 tok/s
    Runs greatqwen3:14b
  • gpt-oss 20BOpenAI's open model. Fast, strong reasoning
    ~160 tok/s
    Runs greatgpt-oss:20b
  • Gemma 3 27BGoogle's best open model for its size
    ~30 tok/s
    Runs greatgemma3:27b
  • Qwen3 30B-A3B30B smarts at small-model speed
    ~160 tok/s
    Runs greatqwen3:30b
  • Qwen3 32BStrong coding and long answers
    ~26 tok/s
    Usableqwen3:32b
  • Llama 3.3 70BClose to the big cloud models
    needs 45 GB
    Too bigllama3.3:70b
  • gpt-oss 120BOpenAI's largest open model
    needs 67 GB
    Too biggpt-oss:120b
  • Qwen3 235B-A22BAlibaba's flagship open model, for multi-GPU rigs
    needs 144 GB
    Too bigqwen3:235b
  • DeepSeek R1 671BThe full DeepSeek R1, for the biggest rigs
    needs 406 GB
    Too bigdeepseek-r1:671b

For a step-by-step setup, see the local image generation guide. Or open the Studio and make your first image in a minute.