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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

Broadest Ecosystem Every layer of the AI stack is accelerated on NVIDIA - CUDA libraries, open-source frameworks, SDKs, and models - with the broadest community and ISV support. Easy-to-Adopt Tools NVIDIA and the open-source community partner closely to optimize popular AI tools like ComfyUI, Hermes, llama.cpp, Ollama, ONNX, OpenClaw, PyTorch, vLLM and more. These tools can be installed easily and run on the same CUDA stack across NVIDIA local and cloud GPUs. Day 0 Support for Latest Models From LLMs to image generators, new open source models run fast on NVIDIA GPUs from day 0 due to close c