Every new LLM architecture comes with its own inference challenges, from transformer models to hybrid vision language models (VLMs) to state space models (SSMs). Turning a reference implementation into a high-performance inference engine typically requires adding KV cache management, sharding weights across GPUs, fusing operations, and tuning the execution graph for specific hardware. AutoDeploy shifts this workflow toward a compiler-driven approach. Instead of requiring model authors to manually reimplement inference logic, AutoDeploy automatically extracts a computation graph from an off-the
Automating Inference Optimizations with NVIDIA TensorRT LLM AutoDeploy | NVIDIA Technical Blog… This avoids the need to bake inference-specific optimizations directly into model code, reducing LLM deployment time. AutoDeploy enables the shift from manually reimplementing and optimizing each model toward a compiler-driven workflow that separates model authoring from inference optimization. …
… 5 MIN READ Mar 12, 2026 Build Accelerated, Differentiable Computational Physics Code for AI with NVIDIA Warp Computer-aided engineering CAE is shifting from human-driven workflows toward AI-driven ones, including physics foundation models that generalize across... …
… Get started with emerging optimizers for LLM training Higher-order optimizers like Muon are proving essential for pushing the boundaries of LLM training efficiency. …
… The new role of LLMs in a heterogeneous AI architecture This doesn’t mean LLMs are obsolete. …