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Showing posts older than #2708 · Back to latest

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Post #2707 719
How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra

https://developer.nvidia.com/blog/how-full-stack-nim-optimizations-deliver-2-5x-more-users-on-nemotron-3-ultra/
NVIDIA Technical Blog How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra Deploying a large language model is only the first step toward production-ready serving. Production teams also need to serve as many concurrent users as possible on available GPU infrastructure while…
Post #2704 443
Physical AI Takes the Wheel: How the World’s Robotaxi Leaders Are Building With NVIDIA Technologies

https://blogs.nvidia.com/blog/robotaxi-leaders-full-stack-open-platform/
NVIDIA Blog Physical AI Takes the Wheel: How the World’s Robotaxi Leaders Are Building With NVIDIA Technologies NVIDIA’s modular, full-stack robotaxi pipeline — a three-computer solution spanning AI training, simulation and in-vehicle computing — is being adopted across the robotaxi ecosystem.
Post #2703 439
d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment

https://blogs.nvidia.com/blog/d-matrix-nvlink-fusion/
NVIDIA Blog d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment AI inference chipmaker d-Matrix today announced it will use NVLink Fusion to connect its next-generation Raptor XPUs to NVIDIA’s AI infrastructure platform — joining a growing roster of ecosystem partners. By connecting Raptor to NVIDIA NVLink scale-up and…
Post #2701 444
From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry

https://developer.nvidia.com/blog/from-wafer-out-to-first-token-codifying-supply-chain-expertise-with-nemotron-and-palantir-foundry/
NVIDIA Technical Blog From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry NVIDIA has one of the largest and most complex supply chains in the world, and its performance is measured from wafer-out to first token. The interval is in two parts. Time-to-rack runs from silicon…
Post #2699 451
When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving

https://developer.nvidia.com/blog/when-to-use-encode-prefill-decode-disaggregation-to-accelerate-multimodal-model-serving/
NVIDIA Technical Blog When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill and decode stages. It is most effective…
Post #2698 472
CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs

https://developer.nvidia.com/blog/cuda-toolkit-13-4-adds-windows-on-arm-support-and-greater-control-over-shared-gpus/
NVIDIA Technical Blog CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs Every NVIDIA CUDA Toolkit release adds functionality and performance improvements that help developers get more from NVIDIA GPUs and the broader NVIDIA software platform. CUDA Toolkit 13.4…
Post #2695 740
Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson

https://developer.nvidia.com/blog/frontier-reasoning-reaches-the-edge-how-to-deploy-and-optimize-models-on-nvidia-jetson/
NVIDIA Technical Blog Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run locally on edge hardware.
Post #2693 605
NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network

https://developer.nvidia.com/blog/nvidia-pair-virtual-inference-router-expands-available-compute-on-your-local-network/
NVIDIA Technical Blog NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network AI agents are learning to do more by working together. A lead agent can break a complex task into smaller jobs and assign those jobs to specialized subagents. Additionally, users are starting to run…
Post #2689 543
The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough

https://developer.nvidia.com/blog/the-modern-cuda-toolbox-in-practice-a-step-by-step-optimization-walkthrough/
NVIDIA Technical Blog The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training. But writing correct, maintainable, and performant CUDA code…
Post #2688 492
Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference

https://developer.nvidia.com/blog/co-designing-ai-models-using-speculative-decoding-for-faster-llm-inference/
NVIDIA Technical Blog Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and offers five guidelines for selecting…
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