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… Spack developers found that using LLMs for writing packages was quite possible given sufficient context and structure provided to the large language model, or as one of the slides in the presentation put it: " LLMs are capable; they need structured guidance to perform reliably ." LLMs can be a time… …
… We also present a set of prompting strategies that proved critical for eliciting high-quality, synthesizable suggestions from the LLM. Image Zoom Fig. 0: The four-phase LLM-aided HLS optimization workflow. Blue nodes are human-driven; Yellow nodes are LLM-driven. …
… 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. …
… Clio isn’t the only legal tech company seeing a massive revenue surge driven by AI. Four-year-old Harvey, which offers LLM AI for law firms, hit ARR of $190 million by the end of 2025, co-founder and CEO Winston Weinberg shared on LinkedIn . …
… At the same time, there’s growing excitement around large language model LLM -driven agents. While powerful, LLMs alone are not enough to deliver autonomous operations. …
… 9 MIN READ Jan 08, 2026 Accelerating LLM and VLM Inference for Automotive and Robotics with NVIDIA TensorRT Edge-LLM Large language models LLMs and multimodal reasoning systems are rapidly expanding beyond the data center. …
… This tends to perform better than having the same LLM call handle both guardrails and the core response. Automating evals for evaluating LLM performance, where each LLM call evaluates a different aspect of the model’s performance on a given prompt. …
… We think responsible innovation is the way to go.” From Your Site Articles “AI Clinician” Makes Treatment Plans for Patients With Sepsis › Google’s New AI Is Learning to Diagnose Patients › Related Articles Around the Web AI-Driven Clinical Decision Support Systems - PMC › OpenEvidence › large lang…
… Ollama served a purpose when local LLMs were new and the tooling around them was rough. …
… Get started with emerging optimizers for LLM training Higher-order optimizers like Muon are proving essential for pushing the boundaries of LLM training efficiency. …