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 Blog… NVIDIA Nsight Streamer improvements now available on NGC for viewing reports on remote headless servers Docker Kubernetes NVIDIA Nsight Operator for Kubernetes improvements - Releasing soon on NGC Learn more here and apply for early access features Feature Table Feature Linux Workstations and Serve… …
… This eliminates the memory swapping overhead of low VRAM mode for an additional performance gain. To get started with multi-GPU inference, check out the llama.cpp GitHub repo and How to Build a Multi-GPU AI PC . …
… What to know before starting Basic comfort with a web browser and file uploads Beginner comfort with running scripts from the command line No ComfyUI experience required—the workflows are pre-built; you load them and run them Familiarity with image concepts layers, masks, alpha channels is helpful,… …
… We’ve put together a guide that walks creators through three production-ready workflows from the GenAI Creator Toolkit, adapted from the NVIDIA GTC 2026 Deep Learning Institute course “Create Generative AI Workflows for Design and Visualization in ComfyUI.” Each workflow is standalone, runs on any … …
… Only available in Linux. Inter-process sharing IPC : Share GPU memory across Python processes without copying through the host. One process allocates, and others map the same physical VRAM into their own address space. …
… Prerequisites Before you begin this tutorial , you’ll need: NVIDIA API Key for cloud-hosted reasoning models get one free Local deployment requires: ~20GB of disk space NVIDIA GPU with at least 24GB of VRAM Operating system with Bash Ubuntu, macOS, or Windows Subsystem for Linux Python 3.10+ enviro… …
… 4 MIN READ Apr 09, 2026 Running Large-Scale GPU Workloads on Kubernetes with Slurm Slurm is an open source cluster management and job scheduling system for Linux. …
… 4 MIN READ Apr 09, 2026 Running Large-Scale GPU Workloads on Kubernetes with Slurm Slurm is an open source cluster management and job scheduling system for Linux. …
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