Researchers and developers often need to execute large language models (LLMs), such as Generative Pre-trained Transformers (GPT), with efficiency and speed. The choice of hardware greatly influences performance during these processing tasks, with the two main contenders being Central Processing Units (CPUs) and Graphics Processing Units (GPUs).
CPUs are standard in virtually all computing devices and are designed to handle various tasks, including operating systems, applications, and certain AI model elements. This versatility allows CPUs to manage tasks requiring logical and sequential processing effectively. However, CPUs fall short when running LLMs due to their architecture. These models require many parallel operations, which CPUs struggle with due to their limited core numbers. While they can run LLMs, the process is far slower than with GPUs, making them less suitable for real-time processing or large model training.
GPUs, initially created for graphics rendering acceleration, have risen as the leading choice for AI and Machine Learning (ML) tasks. They contain hundreds to thousands of smaller cores that can perform numerous operations parallelly. This architecture excels with matrix and vector operations, foundational for ML and LLM operations. GPUs’ parallel processing capabilities provide a sizeable speed advantage over CPUs when training and running LLMs. They can handle more data, execute more operations per second, and significantly reduce the time it takes to train a model or generate responses.
When choosing between a CPU and a GPU for running LLMs locally, several factors come into play. Firstly, the model’s complexity and size can influence the choice, with smaller and simpler models not necessarily requiring GPU power to run efficiently. Secondly, budgetary and resource considerations are crucial as GPUs are typically more expensive and can use more power, potentially needing additional cooling solutions. The development and deployment environment can also play a part as certain environments may offer better support for one processor type over the other. Finally, tasks benefiting from parallel processing significantly perform better on GPUs.
In the context of running LLMs, while CPUs are capable, GPUs offer considerable advantages in speed and efficiency due to their parallel processing capability. This attribute has made GPUs the favored choice for most AI and ML tasks. However, the decision to utilize a CPU or GPU depends on the specific project requirements, such as the model’s complexity, budget limitations, and desired computational speed.
