Best GPU for Stable Diffusion in 2026
SDXL, SD3.5, and Flux benchmarks — ranked by generation speed, VRAM efficiency, and value.
Top Picks for 2026
NVIDIA GeForce RTX 5090 — 32GB Best Overall
The undisputed king of local image generation. 32GB VRAM runs Flux.1 dev at full resolution without quantization. Generates SDXL images in under 1 second with TensorRT optimization. If budget allows, this is the card to get.
Check Price →NVIDIA GeForce RTX 4090 — 24GB Best Value (Used)
Still the sweet spot for Stable Diffusion power users. 24GB handles SDXL, SD3.5 Medium, and Flux.1 (with FP8 quantization). Massive used-market availability has pushed prices down. TensorRT support is mature.
Check Price →NVIDIA GeForce RTX 4060 Ti 16GB Best Budget
Cheapest 16GB card that runs SDXL comfortably. Not the fastest, but 16GB means no OOM errors with SDXL at 1024×1024. Pair with --xformers and Tiled VAE for best results.
Benchmark Results — SDXL 1024×1024 (30 steps)
Benchmarks run with Automatic1111/ComfyUI, default sampler (Euler a), batch size 1. Images/sec measures throughput; lower is slower. Values are estimates based on CUDA core counts, memory bandwidth, and architecture efficiency.
| GPU | VRAM | Images/sec | Sec/image | Affiliate |
|---|---|---|---|---|
| RTX 5090 | 32GB | 3.5 | 0.3s | Check Price |
| RTX 4090 | 24GB | 2.8 | 0.4s | Check Price |
| RTX 5080 | 16GB | 2.2 | 0.5s | Check Price |
| RTX 4080 SUPER | 16GB | 2.0 | 0.5s | Check Price |
| RTX 4070 Ti SUPER | 16GB | 1.8 | 0.6s | Check Price |
| RTX 3090 | 24GB | 1.6 | 0.6s | Check Price |
| RTX 4070 SUPER | 12GB | 1.4 | 0.7s | Check Price |
| RTX 4060 Ti 16GB | 16GB | 0.9 | 1.1s | Check Price |
| RX 7900 XTX | 24GB | 0.8 | 1.3s | Check Price |
| RTX 3060 | 12GB | 0.7 | 1.4s | Check Price |
VRAM Requirements by Model
| Model | Resolution | FP16 VRAM | FP8 VRAM | Notes |
|---|---|---|---|---|
| SD 1.5 | 512×512 | 4GB | — | Runs on anything |
| SDXL / SDXL Turbo | 1024×1024 | 8GB | 6GB | 16GB recommended for batches |
| SD3.5 Medium | 1024×1024 | 10GB | 7GB | 2B parameter model |
| SD3.5 Large | 1024×1024 | 16GB | 10GB | 8B parameter model |
| Flux.1 Schnell | 1024×1024 | 24GB | 12GB | FP8 works with Tiled VAE |
| Flux.1 Dev | 1024×1024 | 24GB | 12GB | NF4 quantization fits 16GB |
AMD vs NVIDIA for Stable Diffusion
That said, AMD cards aren't useless for diffusion:
- RX 7900 XTX (24GB): Best AMD option. Works with ROCm on Linux. Competitive VRAM/$ for large models.
- RX 9070 XT (16GB): New RDNA4. Early ROCm support improving. Watch this space.
- Arc A770 (16GB): Intel's IPEX-LLM works for SD 1.5/SDXL. Limited but improving.
Detailed Recommendations by Budget
Under $400
RTX 3060 12GB — The entry point. Runs SDXL at 1024×1024 without OOM. 12GB is tight for Flux but works with NF4 quantization. Expect ~0.7 images/sec on SDXL.
$400–$700
RTX 4060 Ti 16GB — Best VRAM-per-dollar. 16GB handles SDXL comfortably and Flux.1 with NF4. Slower than 4070 but VRAM matters more than speed for diffusion.
$700–$1,000
RTX 4070 Ti SUPER (16GB) or used RTX 3090 (24GB). The 3090's extra VRAM wins for Flux.1, but the 4070 Ti SUPER is faster for SDXL and draws far less power.
$1,000–$1,800
RTX 4090 (24GB) or RTX 5080 (16GB). The 4090's 24GB is still more useful for diffusion than the 5080's speed. But if you only run SDXL/SD3.5 (not Flux), the 5080 is faster.
$1,800+
RTX 5090 (32GB). End of discussion. 32GB runs everything without quantization. Flux.1 dev at FP16, SD3.5 Large with room to spare. If you generate professionally, this pays for itself in time saved.
Running Flux.1 Efficiently
Flux.1 is the most VRAM-hungry open model in 2026. Here's how to run it on less than 24GB:
- NF4 quantization (16GB cards): Uses ~12GB. Quality drop is minimal. Use ComfyUI with the NF4 checkpoint.
- FP8 (12GB cards): Uses ~10GB. Noticeable quality loss on fine details. Acceptable for drafts.
- Tiled VAE: Saves 2-4GB during decode. Enable in ComfyUI or A1111.
- Gradient checkpointing: Trades speed for VRAM. ~30% slower but fits larger batches.
Frequently Asked Questions
Can I run Stable Diffusion on 8GB VRAM?
Yes. SDXL runs on 8GB with --medvram or --lowvram flags in A1111, or with memory-efficient attention in ComfyUI. SD1.5 runs fine on 8GB. Flux.1 requires NF4 quantization and runs slowly on 8GB.
Is the RTX 3090 good for Stable Diffusion?
Excellent. 24GB VRAM runs Flux.1 dev at FP8 without compromises, SDXL at full precision with large batches, and supports training/DreamBooth. Used at $450-600, unbeatable value for image generation.
How much faster is the RTX 5090 for image generation?
Approximately 1.5-1.8x faster than RTX 4090 for SDXL/SD3.5, and 2x+ faster for Flux.1 due to higher memory bandwidth (1.8TB/s vs 1.0TB/s). The 32GB VRAM also enables larger batch sizes.
Can AMD GPUs run Stable Diffusion?
Yes via DirectML (Windows) or ROCm (Linux). RX 7900 XTX is competitive with RTX 4080 for inference. However, ComfyUI custom nodes and training pipelines often assume CUDA. For best compatibility, NVIDIA is recommended.
What GPU do I need for Flux.1?
24GB+ for comfortable FP8 operation (RTX 3090/4090/5090). 16GB works with NF4 quantization (RTX 4080/5080). 12GB is possible but slow and quality-compromised. For fine-tuning Flux.1, you need 24GB+.