Generative artificial intelligence is rapidly transforming digital content creation, with model sophistication and memory requirements escalating in tandem. The latest Stable Diffusion 3.5 Large model exemplifies this trend, demanding more than 18 GB of VRAM—posing real bottlenecks for widespread deployment across consumer and professional systems. To address this challenge, NVIDIA has pioneered model quantization strategies that allow less critical layers to operate at lower numerical precision, trimming memory needs without a substantial performance hit.
Through a technical partnership with Stability AI, NVIDIA has leveraged FP8 quantization on Stable Diffusion 3.5 Large, slashing VRAM usage by 40 percent. This shift not only reduces the hardware barrier for running sophisticated generative models but also opens doors for higher throughput and efficiency. The NVIDIA GeForce RTX 40 Series and Ada Lovelace RTX PRO GPUs natively support FP8, while the next-generation Blackwell GPUs go further with FP4 precision support, broadening compatibility across NVIDIA´s ecosystem.
NVIDIA TensorRT, central to these advances, brings double the performance to Stable Diffusion 3.5 Large and Medium models by optimizing deep learning workloads. Now redesigned for RTX AI PCs—which have a global installed base exceeding 100 million—TensorRT couples robust inferencing with a just-in-time on-device engine builder, packaging everything in a solution eight times smaller than previous deployments. The release of TensorRT for RTX as a standalone software development kit allows developers direct and streamlined integration, rapidly increasing accessibility to high-powered generative artificial intelligence tools.
