One popular way to run AI locally has been to use multiple GPUs to access more memory and compute. While cloud frameworks like vLLM are well optimized for multiple GPUs thanks to their use in data centers, PC frameworks like llama.cpp and the ComfyUI implementation in PyTorch are not optimized for it. To solve this challenge, NVIDIA has collaborated with both llama.cpp and ComfyUI to enhance performance for RTX PCs with two equivalent GPUs. This enables you to run larger models and use the compute of both GPUs for better performance. llama.cpp now supports tensor parallelism (TP), fully utiliz
Build Personal AI Agents on Windows PCs with New Tools from Microsoft and NVIDIA | NVIDIA Technical BlogNVIDIA Jetson Partner Ecosystem NVIDIA Jetson provides a full AI ecosystem, including software, development tools, and hardware servers, edge appliances, industrial PCs for diverse industries like robotics, manufacturing, retail, transportation, and healthcare, with commercial and ruggedized option… …
… He works with NVIDIA’s developer ecosystem — from app developers to OEMs — to help accelerate their applications and hardware with NVIDIA technology. …
… Windows Workstations and Gaming PCs Linux Workstations and Servers Linux Arm Servers Jetson and Drive Autonomous Machines Operating Systems Windows 10 or newer Ubuntu 24.04, and 22.04 WSL-Ubuntu 2.0 CentOS 7+ RHEL 8, 9, 10 SLES 15 Debian 12 Fedora 41 KylinOS 10 OpenSUSE 15 Rocky 8, 9 Ubuntu 22.04, … …
… Shader compilation for the gaming ecosystem Microsoft and NVIDIA are working to address two major pain points in PC gaming—long compilation times and in-game stuttering—by evolving how shaders are handled. …
… This standalone Windows service manages CloudXR runtime instances through an RPC interface. …
… Readers can explore the broader scheduling landscape through the Cloud Native Computing Foundation CNCF ecosystem. …