Table of Contents
SDXL Turbo Overview
SDXL Turbo is Stability AI’s distilled version of Stable Diffusion XL, designed for real-time image generation in 1-4 inference steps. The distillation process preserves the SDXL architecture while enabling near-instant generation. Running it on a dedicated GPU server is ideal for interactive applications where latency matters. For general Stable Diffusion hosting, SDXL Turbo offers the fastest path to a generated image.
VRAM Requirements by Precision
| Precision | Model Weights | Total VRAM (512×512) | Total VRAM (1024×1024) |
|---|---|---|---|
| FP32 | ~6.9 GB | ~8.5 GB | ~11 GB |
| FP16 / BF16 | ~3.5 GB | ~4.5 GB | ~6.5 GB |
| INT8 | ~1.8 GB | ~3 GB | ~4.5 GB |
| INT4 | ~1.0 GB | ~2.2 GB | ~3.5 GB |
SDXL Turbo shares the same ~3.5B parameter UNet as standard SDXL, so the base model weight sizes are identical. The key difference is the number of inference steps: SDXL Turbo produces usable images in 1-4 steps versus 20-50 for standard SDXL. For the full SDXL VRAM analysis, see our Stable Diffusion VRAM requirements guide.
Resolution and Batch Size Impact
| Resolution | Steps | FP16 VRAM | INT8 VRAM |
|---|---|---|---|
| 512×512 | 1 | ~4.5 GB | ~3.0 GB |
| 512×512 | 4 | ~4.5 GB | ~3.0 GB |
| 768×768 | 1 | ~5.2 GB | ~3.6 GB |
| 1024×1024 | 1 | ~6.5 GB | ~4.5 GB |
| 512×512, batch 4 | 1 | ~8.5 GB | ~6 GB |
SDXL Turbo’s VRAM usage does not scale with step count because the model only loads once. The primary VRAM driver is resolution and batch size. At 512×512 with a single step, FP16 uses just 4.5 GB.
GPU Recommendations
| GPU | VRAM | SDXL Turbo Capability | Max Resolution |
|---|---|---|---|
| RTX 3050 | 6 GB | FP16 up to 768×768 | 768×768 |
| RTX 4060 | 8 GB | FP16 at 1024×1024 + batching | 1024×1024 |
| RTX 4060 Ti | 16 GB | FP16 + large batches | 1024×1024+ |
| RTX 3090 | 24 GB | FP16 + multi-model pipelines | 2048×2048 |
SDXL Turbo is one of the most accessible image generation models for self-hosting. Even the RTX 3050 can run it at 768×768 in FP16. The RTX 4060 handles 1024×1024 with room for batch generation.
Comparison with SDXL and Flux
SDXL Turbo uses the same VRAM footprint as standard SDXL but generates images 5-20x faster due to the reduced step count. Compared to Flux.1, SDXL Turbo requires roughly half the VRAM and is significantly faster, though Flux produces higher-quality results with better prompt adherence. See our Flux.1 VRAM requirements for detailed Flux sizing.
For interactive prototyping, SDXL Turbo is the best choice. For final production images, consider SDXL with 30 steps or Flux.1 Dev. Compare all image generation VRAM needs in the GPU for inference guide.
Deployment Recommendations
SDXL Turbo excels at real-time preview generation and interactive editing workflows. Deploy it on a budget RTX 4060 for single-user interactive use, or on an RTX 3090 for multi-user serving. Pair it with ComfyUI for a node-based editing interface.
Use the GPU comparisons tool to evaluate options. Estimate costs with the cost calculator. Browse all image generation guides in the model guides section.
Deploy SDXL Turbo on Dedicated GPUs
Run real-time image generation with SDXL Turbo on budget-friendly dedicated GPU servers. From 6 GB to 24 GB VRAM options available.
Browse GPU Servers
