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Prompt

I've never really understood why textual AI applications like LLMs require intensive GPU resources specifically. My primitive understanding is that GPUs are useful for graphic applications (video etc). What is it about LLM workloads that makes them particularly require this hardware rather than (say) what I would have assumed which is that they require intensive computing resources / high end CPUs?

Output

GPUs (Graphics Processing Units) have become essential for running large language models (LLMs) and other AI applications due to their unique architecture and capabilities. While GPUs were originally designed for graphics processing, their characteristics make them particularly well-suited for AI workloads. Here's why:

Parallel Processing

The most significant advantage of GPUs for AI applications is their ability to perform massive parallel computations:

  • GPUs have thousands of cores designed to work simultaneously, while CPUs typically have fewer cores[1].
  • LLMs involve extensive matrix calculations that can be efficiently parallelized, making GPUs ideal for these operations[5].
  • This parallel architecture allows GPUs to process multiple data points concurrently, greatly speeding up the computations required for LLMs[3].

Specialized for AI Workloads

Over time, GPU manufacturers have optimized their hardware specifically for AI and machine learning tasks:

  • Modern GPUs include Tensor Cores, which are up to 60 times more powerful than first-generation designs for processing the matrix math used in neural networks[1].
  • These specialized cores are highly efficient at handling the types of calculations common in AI models, including the transformer architectures used in many LLMs[1].

Memory Bandwidth and Efficiency

GPUs are designed to handle large amounts of data quickly:

  • They typically have much higher memory bandwidth than CPUs, which is crucial when processing the vast amounts of data involved in training and running LLMs[3].
  • GPU memory hierarchies are optimized for high throughput of large data blocks, which is beneficial for AI tasks[3].

Performance Advantages

The combination of these factors results in significant performance gains:

  • GPUs can perform technical calculations faster and with greater energy efficiency than CPUs for AI workloads[1].
  • For large language models, GPUs and TPUs (Tensor Processing Units) are generally 1-3 orders of magnitude more cost-effective than leading-node CPUs when considering production and operating costs[1].

Scalability

GPU systems can be scaled up to meet the demands of increasingly large AI models:

  • Multiple GPUs can be used in parallel to further increase computing power[4].
  • This scalability is crucial as the size of AI models and datasets continues to grow[1].

While CPUs still have a role in AI workflows, particularly for preprocessing, postprocessing, and certain types of algorithms, GPUs have become the preferred hardware for training and running large language models due to their superior performance in handling the parallel, matrix-heavy computations that characterize these AI workloads.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.