Speculative Decoding for Much Faster LLMs

Author(s): M

Originally published on Towards AI.

How to make LLMs 3x faster without losing quality.

Large language models are slow. When you ask ChatGPT or Claude a question, you wait as words come out one by one. This isn’t just frustrating for users; it’s also costly. Companies spend millions on GPUs to run these models, and a significant portion of that expense is due to a single bottleneck.

This article explores the challenges faced by large language models (LLMs) in terms of speed and efficiency due to memory bandwidth limitations. It introduces speculative decoding as a solution, allowing for faster inference without sacrificing quality. Through a dual-model approach, where a smaller draft model predicts potential next tokens and a larger model verifies them, the technique significantly reduces processing time. The article also discusses its practical applications, variants, and implementation complexities while highlighting its significance in the evolving landscape of AI efficiency.

Read the full blog for free on Medium.

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Published via Towards AI