SW/HW Co-optimization Technique for Massive Language Fashions (LLMs) #Imaginations Hub

SW/HW Co-optimization Technique for Massive Language Fashions (LLMs) #Imaginations Hub
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Tips on how to stretch each bit out of your system to run LLMs quicker? — greatest apply

Leading Massive Language Fashions (LLMs) like ChatGPT, Llama, and so on. are revolutionizing the tech trade and impacting everybody’s lives. Nevertheless, their value poses a big hurdle. Functions using OpenAI APIs incur substantial bills for steady operation ($0.03 per 1,000 immediate tokens and $0.06 per 1,000 sampled tokens).

To chop prices, firms are likely to host their very own LLMs, with bills various extensively primarily based on mannequin dimension (bigger LLMs with 100–200B parameters can value ~10 instances extra in comparison with smaller ones with 7–15B parameters). This pattern has spurred the AI chip race, as main tech firms goal to develop their very own AI chips, lowering reliance on costly {hardware}.

Development of mannequin dimension. Supply: AWS reInvent

Tips on how to squeeze each little bit of computing energy to run LLMs? On this article, I’m going to do an intensive evaluation of LLM optimization technique throughout fashions, software program, and {hardware}. It follows the AI SW/HW co-design methodology I wrote in earlier article, with way more in-depth dialogue on LLM-specific value and efficiency optimization.

Supply: made by creator and different colleagues

The compute and reminiscence calls for of operating LLM fashions are rising exponentially, whereas computing/reminiscence capabilities are lagging behind on a slower trajectory, as depicted within the picture above. To bridge this efficiency hole, it’s essential to discover enhancements in three key areas:

  1. Algorithmic Enchancment and Mannequin Compression: How can we increase fashions with options to scale back compute and reminiscence calls for with out compromising high quality? What are the most recent developments in LLM quantization expertise that scale back mannequin dimension whereas sustaining high quality?
  2. Environment friendly SW Stack and Acceleration Libraries: What issues are important in…


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