NVIDIA TensorRT for RTX introduces adaptive inference, a breakthrough that eliminates the traditional trade-off between performance and portability in AI deployment. Designed for consumer-grade hardware, this lightweight inference library under 200 MB enables Just-In-Time (JIT) compilation in under 30 seconds, making it ideal for real-time AI applications on RTX-powered devices. Adaptive inference allows engines to automatically optimize at runtime based on the user’s specific system. As the application runs, it compiles GPU-specific kernels, learns from workload patterns, and improves performance over time—all without any developer intervention. This means developers can build a single, portable engine once and deploy it across diverse hardware, with performance that evolves and improves with use. Three core features drive this self-optimization: Dynamic Shapes Kernel Specialization, built-in CUDA Graphs, and runtime caching. Dynamic Shapes Kernel Specialization automatically generates and caches optimized kernels for actual input shapes encountered during inference, replacing generic fallbacks. This delivers consistent speedups across models with variable input dimensions, such as different image resolutions or batch sizes. Built-in CUDA Graphs eliminate per-kernel launch overhead—typically 5–15 microseconds—by capturing the entire inference sequence as a single execution graph. This is especially effective for models with many small operations, where launch time can dominate total latency. On an RTX 5090 with Hardware-Accelerated GPU Scheduling enabled, this can yield up to a 23% performance boost, reducing inference time by 1.8 ms per run. Runtime caching further enhances performance by preserving compiled kernels across application sessions. After initial runs generate optimized kernels for common shapes, developers can serialize these into a binary cache file. Loading this file in future sessions skips compilation entirely, enabling peak performance from the first inference. The cache can even be bundled with the app, pre-generated for specific platforms, ensuring instant optimization on user devices. Performance benchmarks show adaptive inference outperforming static optimization. On the FLUX.1 [dev] model at 512×512 resolution with dynamic shapes, adaptive inference surpasses static optimization by iteration 2 and reaches 1.32x faster with all features enabled. JIT compilation time drops from 31.92 seconds to just 1.95 seconds—a 16x improvement—thanks to cached specializations. This approach shifts the workflow from manual, static tuning to automatic, adaptive optimization. Developers no longer need to predefine multiple build targets or predict input shapes. Instead, they focus on building flexible, high-performance applications that deliver optimal results across a wide range of hardware. For developers, this means faster iteration, simpler deployment, and better end-user experiences. To get started, explore the NVIDIA/TensorRT-RTX GitHub repository and try the FLUX.1 [dev] Pipeline Optimized with TensorRT RTX notebook. A live walkthrough video demonstrates these features in action, showing real-time performance gains across diffusion models. With adaptive inference, NVIDIA TensorRT for RTX empowers developers to build AI applications that run faster, more efficiently, and more privately on-device—without sacrificing flexibility or requiring complex configuration.