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… Day 0 fine-tuning with NeMo Framework Developers can customize Gemma 4 with their own domain data using the NVIDIA NeMo framework , specifically the NeMo Automodel library, which combines native PyTorch ease of use with optimized performance. …
… Download for Windows Download for Linux Download for Linux Arm64 Capture GPU Pipeline Exceptions Nsight Aftermath SDK generates a GPU “mini-dump” file from a live graphics application, containing detailed information about the GPU pipeline. …
… This post introduces new JetPack 7.2 release features and capabilities, which also include: NVIDIA Multi-Instance GPU MIG support on NVIDIA Jetson Thor for deterministic multiworkload execution Official Yocto Project support for custom Linux distributions that can further improve system efficiency … …
… Updates to NVIDIA NemoClaw, Hermes Agent, and H Companys Holo 3.1 models expand agent capabilities across Windows and Linux, improving ease of setup, native app integration, and performance on NVIDIA GPUs. …
… Measure CPU memory usage Use procrank to analyze memory usage: $ git clone https://github.com/csimmonds/procrank linux.git $ cd procrank linux/ $ make $ sudo ./procrank The output is sorted by PSS Proportional Set Size , reflecting actual physical memory usage. …
… For performance enthusiasts, the newly launched NVIDIA CompileIQ compiler auto-tuning framework delivers up to a 15% speedup on critical kernels like GEMM and attention. …
… The IGX Thor family includes purpose-built modules and kitssuch as IGX T5000 SoM, IGX T7000 Board Kit, and Developer Kitsoffering scalable AI performance, industrial-grade hardware, real-time Linux, rich I/O, and compatibility with both NVIDIA Jetson hardware and software stacks. …
… It also gives us tighter control over CRIU for performance tuning and allows checkpoint artifacts to live in flexible storage backends instead of being embedded into OCI images. …
… Many teams invest weeks fine-tuning models, only to discover that exporting to a deployment format breaks layers, input shapes cause runtime failures, or version mismatches silently degrade performance. …
… Presented 11-11-2020 | Supercomputing 2020: Nsight Compute 2020.2 CUDA 11.1 | New in 2020.1 | View on YouTube Nsight Compute 2020.1 Spotlight This NVIDIA Nsight Compute 2020.1 release spotlight highlights these new features: Roofline Analysis for Visualization of Performance Headroom NVIDIA Ampere … …