TL;DR: Running Stable Diffusion and local LLMs on the same PC is absolutely doable, but both workloads are VRAM-hungry. A single 24GB GPU handles it with swapping. Two GPUs — one dedicated to each task — is the real answer for running both simultaneously. If you're buying one card, get an RTX 3090 or 4090. If you want simultaneous workflows, pair an RTX 5060 Ti 16GB with whatever 24GB card you can afford.
The Dual-Workload Problem
Here's why this is tricky: both Stable Diffusion and LLM inference want to live in VRAM full-time.
Stable Diffusion XL at default settings (1024x1024, 20 steps) uses about 8-11GB of VRAM. A LoRA fine-tune or ControlNet pipeline can push that to 12-14GB. Meanwhile, running a useful LLM — say a 13B model at Q4_K_M quantization — needs another 8-10GB of VRAM.
If both try to occupy the same GPU, one has to unload before the other can run. That means 10-30 seconds of waiting every time you switch between generating images and chatting with your LLM. Doable, but annoying if you're switching frequently.
The math is simple: 8-14GB for image generation + 8-20GB for your LLM = you need 16-34GB of VRAM to run both comfortably. No single consumer GPU gives you that headroom without compromise — except the RTX 5090 at 32GB, and even that gets tight with larger LLMs.
VRAM Minimums: What Each Workload Actually Needs
Stable Diffusion:
- SD 1.5: 4-6GB VRAM (lightweight, older, still popular for LoRA work)
- SDXL: 8-11GB base, 12-14GB with ControlNet or LoRA stacks
- Flux: 10-16GB depending on version and optimizations
- ComfyUI with --lowvram flag: Can squeeze SDXL into 4GB, but generation times double or triple
LLM Inference:
- 7B model (Q4): ~4.5GB — fits alongside SD 1.5 on a 12GB card
- 13B model (Q4): ~8GB — needs 16GB+ alongside any image gen
- 27-34B model (Q4): ~16-20GB — realistically needs its own card
- For more detail, see our complete VRAM breakdown by model size
The floor: 16GB of total VRAM gets you running both workloads, but only one at a time with frequent swapping. 24GB is where it starts feeling comfortable for sequential use. Simultaneous use needs either 32GB on one card or two separate GPUs.
Option 1: Single GPU (Time-Sharing)
If you run one workload at a time — generate your images, then switch to your LLM — a single high-VRAM card works fine.
RTX 3090 (24GB) — ~$800 used (as of March 2026)
The best bang-for-buck option. 24GB lets you run SDXL at full quality and still have room for a 7B-13B model. You'll unload one before loading the other, but with Ollama or llama.cpp, model loading takes 3-5 seconds. Not bad.
The 3090 handles SDXL at 1024x1024 in about 4-6 seconds per image at 20 steps. LLM inference with a 13B Q4 model runs at roughly 40-50 tokens per second. Both are perfectly usable.
RTX 4090 (24GB) — ~$1,600 (as of March 2026)
Same 24GB, faster everything. SDXL renders in 2-3 seconds per image. LLM inference hits 90-100+ t/s on 13B models. If you're doing heavy image generation (hundreds of images in a batch), the speed difference over the 3090 is substantial.
RTX 5090 (32GB) — ~$2,000 (as of March 2026)
The only single-card option where you can realistically keep a small LLM loaded while generating images. 32GB means SDXL (11GB) + a 13B Q4 model (8GB) = 19GB, leaving headroom. You won't hit peak performance on both simultaneously, but it works. The 1,790 GB/s bandwidth makes LLM inference noticeably snappier than the 4090.
Option 2: Dual GPUs (The Real Answer)
If you regularly switch between image generation and LLM chat — or want both running at the same time — two GPUs is the way.
The concept: dedicate one GPU to Stable Diffusion and the other to your LLM. Both stay loaded in their respective VRAM pools. No swapping, no waiting.
Budget Dual Setup: RTX 3060 12GB + RTX 3090 24GB — ~$1,050 total (used)
- RTX 3060 12GB (~$250 used): Handles SD 1.5 and SDXL (with --medvram optimization) comfortably
- RTX 3090 24GB (~$800 used): Runs your LLM up to 34B at Q4
This is the sweet spot for most people. The 3060 handles image generation at perfectly usable speeds (8-12 seconds per SDXL image), and the 3090 gives you serious LLM headroom. Total cost is barely more than a single 4090.
Mid-Range Dual Setup: RTX 5060 Ti 16GB + RTX 4090 24GB — ~$2,030 total
- RTX 5060 Ti 16GB (~$430): Handles all Stable Diffusion workloads including Flux with room to spare
- RTX 4090 24GB (~$1,600): Fast LLM inference on anything up to 34B
This gives you excellent image generation speed on the 5060 Ti (the 16GB GDDR7 handles even demanding ComfyUI workflows) and top-tier LLM performance on the 4090.
High-End Dual Setup: RTX 4090 + RTX 4090 — ~$3,200 total
If budget isn't the primary concern, two 4090s give you 48GB total VRAM. You can dedicate 24GB to image gen (overkill but fast) and 24GB to a 34B LLM. Or split models across both cards for 70B inference. See our dual-GPU build guide for the full breakdown.
Recommended Builds for Combined Workflows
The Practical Build (~$1,500 total)
- GPU: RTX 3090 24GB (used, ~$800)
- CPU: AMD Ryzen 7 7700X (~$220)
- RAM: 32GB DDR5-5600 (~$80)
- PSU: 850W 80+ Gold (~$120)
- Storage: 1TB NVMe Gen4 (~$70)
- Motherboard: B650 with two x16 PCIe slots (~$150)
Runs SDXL and 13B-34B LLMs. One workload at a time, fast swapping. Handles 90% of creative AI workflows without breaking a sweat.
The Simultaneous Build (~$2,200 total)
- GPUs: RTX 3060 12GB (
$250 used) + RTX 3090 24GB ($800 used) - CPU: AMD Ryzen 7 7800X3D (~$300)
- RAM: 64GB DDR5-5600 (~$160)
- PSU: 1000W 80+ Gold (~$160)
- Storage: 2TB NVMe Gen4 (~$120)
- Motherboard: X670E with two x16 PCIe slots (~$200)
Both workloads run simultaneously. Image gen on the 3060, LLM on the 3090. The 64GB system RAM gives headroom for large context windows and ComfyUI's workflow caching.
Power Supply Warning
Dual GPUs draw serious power. An RTX 3090 alone pulls 350W. Add a 3060 at 170W, plus CPU and everything else, and you're looking at 600-700W under full load. Don't cheap out on the PSU — get at least 1000W for any dual-GPU setup, 1200W if you're pairing two high-end cards.
Software Setup Tips
For Stable Diffusion (ComfyUI or Automatic1111):
Set the CUDA_VISIBLE_DEVICES environment variable to lock image gen to a specific GPU. In ComfyUI, use --cuda-device 1 to target the second card.
For LLM Inference (Ollama or llama.cpp):
Ollama defaults to GPU 0. Set CUDA_VISIBLE_DEVICES=0 before launching to keep it on your primary card. In llama.cpp, use the --gpu-layers flag with --main-gpu to target a specific device.
The key: make sure each application knows which GPU it should use. Without explicit assignment, both will fight over GPU 0, and your second card sits idle.
Should You Just Buy a Mac Instead?
A Mac Studio M4 Max with 128GB unified memory can run both Stable Diffusion (via Draw Things or ComfyUI Metal) and 70B+ LLMs simultaneously, all in the same memory pool. No VRAM juggling, no dual-GPU complexity. It's elegant.
The trade-off: image generation on Apple Silicon is slower than a dedicated NVIDIA GPU, and you're locked into Metal-compatible SD implementations. For pure image gen speed, NVIDIA still wins. But if simplicity and running massive LLMs alongside image gen matters more than raw SD throughput, Apple Silicon is worth a look. See our M4 Max vs RTX 4090 comparison for the full picture.
Bottom Line
Running Stable Diffusion and LLMs on the same PC comes down to how you use them:
- Sequential use (one at a time): Single RTX 3090 ($800) or RTX 4090 ($1,600). Simple, effective.
- Simultaneous use: Two GPUs. Budget option is a used 3060 + 3090 for ~$1,050. Worth it if you switch between workflows frequently.
- Money-is-no-object: RTX 5090 (32GB) for single-card flexibility, or dual 4090s for maximum performance on both workloads.
The dual-workload PC is one of the best arguments for building your own rig instead of buying a prebuilt. You get to pick exactly the GPU combo that fits your workflow — and you avoid paying the "AI workstation" markup that OEMs love to charge.
For the full GPU comparison, check our best GPUs for local LLMs guide. For budget-conscious builds at every price point, see our budget guide.