We're excited to announce our collaboration with NVIDIA to launch the Stable Diffusion 3.5 NIM microservice, enabling significant performance improvements and streamlined enterprise deployment for our leading image generation models.The SD3.5 NIM supports Stable Diffusion 3.5 Large, with expanded model compatibility planned for future releases.
The SD3.5 NIM delivers faster image generation on enterprise hardware. This enables complex image generation workflows that weren’t as feasible before.
A NIM provides a simplified, optimized way to run AI inference by packaging inference engines, APIs, and model configurations into secure, portable containers. Think of it as a pre-configured, enterprise-ready package that eliminates the complexity of setting up and optimizing AI models from scratch.
The SD3.5 NIM delivers performance gains that improve efficiency and ease of deployment for enterprises:
Speed improvements: 1.8x performance gains over PyTorch, with NVIDIA H100 GPUs testing showing TensorRT-optimized generation at 3,700ms compared to 6,800ms for standard PyTorch on SD3.5 Large.
Consolidated deployment: The SD3.5 NIM supports the SD3.5 Large model with Depth and Canny ControlNets within a single container, meaning that instead of needing separate deployments for each model, users get all versions packaged together.
The SD3.5 NIM supports enterprise and data center Ada and Blackwell GPUs.
These optimizations enable faster iteration cycles, larger batch processing, and more complex workflows that were previously less practical due to hardware limitations.
The efficiency gains are particularly valuable for advanced workflows, such as running multiple models simultaneously. The performance improvements make complex, multi-model workflows more feasible, opening new possibilities for advanced users and making rapid scaling a simpler option when utilizing cloud deployments.
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