SDXL VRAM Requirements (Base, Refiner, Turbo)

Before you deploy SDXL on a dedicated GPU server, you need to know exactly how much VRAM each variant consumes at different precisions. This guide gives you the real numbers — measured on GigaGPU dedicated servers — so you can match your model to the right hardware without guessing.

VRAM by Variant and Precision

Each row shows the minimum VRAM needed to load the model weights. Add 10-20% headroom for KV cache, activations, and batch processing.

Which GigaGPU Server Fits SDXL?

Based on the VRAM table above, here’s how SDXL maps to our GPU lineup:

Context Length Impact

VRAM requirements scale with context length. A 32K context adds roughly 2-4 GB of KV cache on top of base weights. For 128K contexts on large variants, you may need to move up a GPU tier or use quantised KV cache. See our context length VRAM guide for details.

Deployment Recommendations

For production deployments:

• Development & prototyping: Use INT4 on the smallest GPU that fits — minimise cost while you iterate.
• Production inference: Use FP16 on a GPU with at least 20% headroom. This avoids OOM under batch load.
• High-throughput serving: Step up to a larger GPU to batch more requests simultaneously.

Our best GPU for LLM inference guide walks through the full decision matrix across every workload type.

For cost analysis, use our LLM cost calculator or check cost per million tokens by GPU.