From NVIDIA: 2024-10-23 09:00:10

Large language models (LLMs) are revolutionizing productivity by drafting documents, summarizing web pages, and answering questions accurately. Some LLMs can run locally on PCs, while others require data centers due to size. GPU offloading allows part of a prompt to run on a data-center-class model locally on RTX-powered PCs.

Tradeoff exists between model size, response quality, and performance. Larger models offer higher quality but run slower. Smaller models sacrifice quality for performance. GPU offloading divides the model between GPU and CPU, maximizing GPU acceleration regardless of model size.

LM Studio app allows hosting LLMs on desktops or laptops with customizable interfaces. GPU offloading divides the model into subgraphs processed by the GPU, boosting performance. Users can assess the impact of GPU offloading on performance, with increasing throughput compared to running on CPUs alone.

LM Studio’s GPU offloading unlocks the full potential of LLMs on RTX AI PCs, making larger, complex models accessible. Users can download LM Studio to try GPU offloading on larger models or experiment with RTX-accelerated LLMs locally on RTX AI PCs and workstations. Subscribe to AI Decoded newsletter for more AI updates.



Read more at NVIDIA: Accelerate Larger LLMs Locally on RTX With LM Studio