SDXL + LLM Pipeline on RTX 5090: Performance Benchmark & Cost, Category: Benchmarks, Slug: sdxl-llm-pipeline-on-rtx-5090-benchmark, Excerpt: SDXL + LLM Pipeline benchmarked on RTX 5090: Stable Diffusion XL + LLaMA 3 8B, concurrent performance, VRAM breakdown, and cost analysis., Internal links: 9 –>

Want an LLM to write your image prompts and SDXL to render them, all on one GPU without memory gymnastics? The RTX 5090 (32 GB VRAM) finally gives this combination the breathing room it needs. We tested Stable Diffusion XL and LLaMA 3 8B running simultaneously on a GigaGPU dedicated server — here are the concurrent performance numbers.

Models tested: Stable Diffusion XL + LLaMA 3 8B

Dual-Workload Performance

All models loaded simultaneously in GPU memory. Throughput figures reflect concurrent operation with shared VRAM and compute.

VRAM Allocation

Where the RTX 3090 buckled under zero headroom with this combination, the 5090 handles it with 6.5 GB to spare. That buffer accommodates higher-resolution SDXL generation, extended LLM context for longer prompt crafting sessions, and the general overhead of concurrent CUDA kernels. It is a night-and-day difference in operational stability compared to the 3090.

Is It Worth the Premium?

At £299/mo, the 5090 costs double the 3090 but delivers more than double the concurrent throughput (4.08 vs 1.92 it/sec for SDXL, 55.0 vs 34.1 tok/s for the LLM). If you are building an AI art product where users type a description and receive an image, the 5090 makes the round-trip fast enough for interactive use. Explore all options at our benchmark page.

Creative AI on One Card

The SDXL + LLM pairing on the 5090 enables workflows that were previously multi-server: the LLM refines vague user requests into detailed SDXL prompts, generates image descriptions for SEO, or provides style guidance based on reference images. With 55 tok/s generation and 4.08 it/sec image diffusion happening in parallel, you can build responsive creative tools without the infrastructure complexity of split GPU deployments. For even more demanding image models like Flux.1, see our Flux + LLM pipeline benchmark.

Quick deploy:

docker compose up -d  # comfyui + llama.cpp containers with --gpus all

See our LLM hosting guide, GPU server options, and all benchmark results. Related benchmarks: LLaMA 3 8B on RTX 5090, SDXL on RTX 5090.