How LM Studio Accelerates Larger LLMs Quickly On RTX
How LM Studio Accelerates Larger LLMs Quickly On RTX
Digital assistants, conversational avatars, and customer support agents are just a few of the new applications of generative AI that rely heavily on LLMs
Users may use AI without the internet, keep chats and content secret on-device, or just utilize the potent NVIDIA GeForce RTX GPUs in their system
There is a trade-off between performance, response quality, and model size. Larger machines typically produce better results but operate more slowly
There is a trade-off between performance, response quality, and model size. Larger machines typically produce better results but operate more slowly
Tens of gigabytes in size, the most accurate LLMs are made to operate in a data center and might not fit in a GPU’s memory. Normally, this would stop the application from utilizing GPU acceleration
But with GPU offloading, some of the LLM is used on the CPU and some on the GPU. This enables users, irrespective of model size, to fully benefit from GPU acceleration
The number of parameters in the model, shown by “27B,” provides an estimate of the amount of memory needed to run the model
The number of parameters in the model, shown by “27B,” provides an estimate of the amount of memory needed to run the model
The GeForce RTX 4090 desktop GPU has 19GB of VRAM, which is needed to fully accelerate this model on the GPU. The model can operate on a system with a less powerful GPU and still gain acceleration with to GPU offloading