New Inference Framework Speeds up LLMs Without Raising Costs

New Inference Framework Speeds up LLMs Without Raising Costs

Large language models (LLMs) are some of today’s most impactful technologies. They’re what make advanced chatbots and generative AI possible, but as their functionality grows, so too do their costs and complexity. A new framework from Stanford researchers could change that.

In a recent research paper, a team unveiled a modular inference framework called Archon. Inference is the stage where LLMs draw on what they’ve learned in training to determine appropriate responses or make predictions based on new data. This requires a considerable amount of complicated computing, so it’s often either slow or expensive. Archon speeds it up without raising costs. Read more.

Power Modules Result in Smaller (and Lighter) Vehicles

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Godwin Josh

Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer

2 周

The focus on efficiency gains in LLMs through new inference frameworks raises questions about whether this prioritizes speed over other crucial aspects like explainability and bias mitigation. Recent research on "AI for Social Good" emphasizes the need for ethical considerations alongside performance improvements. How would these power modules impact the accessibility of electric vehicles in developing nations with limited infrastructure?

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