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How LM Studio Accelerates Larger LLMs Quickly On RTX

LM Studio

The AI Decoded series introduces new hardware, software, tools, and accelerations for GeForce RTX PC and NVIDIA RTX workstation users and demystifies AI.

Productivity is changing as a result of large language model (LLM). They can write documents, summarize webpages, and, with extensive training on a wide range of data, provide precise answers to inquiries about almost any subject.

Digital assistants, conversational avatars, and customer support agents are just a few of the new applications of generative AI that rely heavily on LLMs.

Many of the most recent LLMs are capable of operating locally on workstations or PCs. For a number of reasons, this is helpful: 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. Other models require hardware at huge data centers due to their size and complexity, which prevents them from fitting into the local GPU’s visual memory (VRAM).

However, RTX-powered PCs can use a method called GPU offloading to locally speed up a portion of a task on a data-center-class model. As a result, users can take advantage of GPU acceleration without being constrained by GPU memory.

Size and Quality vs. Performance

There is a trade-off between performance, response quality, and model size. Larger machines typically produce better results but operate more slowly. Performance increases while quality decreases with smaller models.

It’s not always easy to make this trade-off. In certain situations, performance may be more crucial than quality. Because it can operate in the background, certain users might place a higher priority on accuracy for use cases like content production. On the other hand, a conversational assistant must respond accurately and quickly.

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.

Optimize AI Acceleration With GPU Offloading and LM Studio

With an intuitive interface that enables a great deal of modification in the way those models function, LM Studio is a program that enables users to download and host LLMs on their desktop or laptop computer. Since LM Studio is based on llama.cpp, it is completely compatible with GeForce RTX and NVIDIA RTX graphics cards.

Even if the model cannot be fully loaded into VRAM, LM Studio and GPU offloading use GPU acceleration to improve the speed of a locally hosted LLM.

LM Studio separates the model into smaller sections, or “subgraphs,” which stand in for layers of the model architecture, in order to facilitate GPU offloading. Subgraphs are loaded and unloaded on the GPU as needed rather than being fixed there permanently. The GPU offloading slider in LM Studio allows users to control how many of these layers are handled by the GPU.

Consider employing this GPU offloading method on a large model, such as the Gemma 2 27B. The number of parameters in the model, shown by “27B,” provides an estimate of the amount of memory needed to run the model.

Each parameter uses half a byte of memory, according to 4-bit quantization, a method for shrinking an LLM without appreciably lowering accuracy. Accordingly, the model should need roughly 13.5 billion bytes, or 13.5GB, plus some overhead, which typically falls between 1 and 5GB.

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.Image credit to NVIDIA

It is feasible to evaluate the performance impact of varying GPU offloading levels in LM Studio in comparison to CPU-only. The results of executing the same query on a GeForce RTX 4090 desktop GPU at various offloading settings are displayed in the table below.Image credit to NVIDIA

Even customers with an 8GB GPU can get a noticeable speedup on this specific model as compared to those who solely utilize CPUs. Naturally, a smaller model that completely fits in GPU memory can always operate on an 8GB GPU and receive full GPU acceleration.

Achieving Optimal Balance

The GPU offloading feature of LM Studio is an effective tool for maximizing the performance of data center-specific LLMs, such as Gemma 2 27B, locally on RTX AI PCs. It enables access to larger, more intricate models on all PCs with GeForce RTX and NVIDIA RTX GPUs.

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