…HiSim guides SGLang R&D by allowing configuration tuning to quantify scheduling tradeoffs (TTFT/throughput, queueing/memory, cache hit/TTFT, overlap efficiency) without code changes. It provides “oracle” evaluation for new hardware…
…AI Agent | AI-Ready Data | Cloud Services About the Authors About Dhruv Desai Dhruv Desai is a senior solutions architect at NVIDIA, focusing on capital markets. His background spans quantitative finance and…
…NVIDIA NeMo Megatron Bridge provides production-ready low-precision training recipes that allow seamless switching between precision formats, supporting efficient large-scale model training with minimal code modifications. AI-generated content may…
…from AI interacting with AI. Modern agentic systems plan tasks, invoke tools, execute code, retrieve data, and coordinate across continuous multistep workflows with numerous AI agents . These interactions generate large volumes of…
…RoboLab also comes with agent skills that can be leveraged by a coding agent to generate novel tasks directly in a user’s workflow. This efficiency also future-proofs the benchmark: new…
…Running models with tens of billions of parameters in production, especially for conversational or voice-based AI agents, demands high throughput, low latency, and predictable service-level performance. For startups building sovereign…
…Download the code # Clone the repository git clone https://github.com/NVIDIA-Digital-Bio/Proteina-Complexa cd Proteina-Complexa Step 2: Set up the environment Using UV package manager: # Create a virtual…
…To learn more, see Boost GPU Memory Performance with No Code Changes Using NVIDIA CUDA MPS . Discuss (0) Discuss (0) Tags Agentic AI / Generative AI | Data Center / Cloud | Simulation / Modeling / Design | General…
…CompileIQ is not a magic tool that automatically turns poorly-written code into high-performing code. To get the best value from CompileIQ, you need to start with reasonably high-performing code…
…In the style of Karpathy’s autoresearch , an agentic harness can propose a nontrivial code change, rebuild Dynamo, rerun the same trace, and keep only changes that improve the objective. That turns…