The 2024 Guide to NVIDIA GPUs for LLM Inference — What you need to know
Balancing Performance and Cost for Efficient LLM Inference.
Introduction
Large Language Models (LLMs) such as GPT-4, GPT-4o, and BERT have transformed the field of artificial intelligence, particularly in natural language processing tasks. These sophisticated models require substantial computational power not only for training but also for inference. Selecting the appropriate GPU for LLM inference is crucial, as it can significantly affect performance, cost-efficiency, and scalability.
In this guide, we’ll investigate the top NVIDIA GPUs suitable for LLM inference tasks. We’ll compare them based on key specifications like CUDA cores, Tensor cores, VRAM, clock speeds, and pricing. Whether you’re working on a personal project, setting up a research lab, or deploying at scale in a production environment, this article will help you make an informed decision. Before we dive into the details, let us take a look at some trends in the industry.
Emerging GPU Trends and Alternatives for LLM Inference
As the demand for LLM inference grows, several new trends in GPU architecture and alternative hardware solutions are emerging. These innovations aim to make AI more accessible, scalable, and…
