In recent years, advancements in large language models (LLMs) have improved chatbots’ ability to understand customer queries effectively. However, the high cost and slow delivery of services using LLMs have hindered their widespread adoption. To address these challenges, researchers have developed speculative decoding, an optimization technique that accelerates AI inference, reducing latency and improving customer experiences.
Speculative decoding involves modifying the forward pass of an LLM to evaluate multiple potential tokens simultaneously, significantly enhancing token generation speed. By using a smaller, more efficient model or integrating it into the main model, speculative decoding can increase inference speed two to three times, making interactions with chatbots smoother for users. IBM researchers have enhanced speculative decoding by eliminating the need for a separate draft model and refining the process with efficient training methods, leading to significant performance gains.
One key issue with reducing latency in LLMs is the impact it has on memory and throughput. To address this, IBM researchers combined speculative decoding with paged attention, an optimization technique that optimizes memory usage. Paged attention divides key-value sequences into smaller blocks, minimizing redundant computation and freeing up memory space for speculative decoding. By integrating these techniques into IBM’s Granite 20B code model, researchers achieved significant performance improvements.
The IBM speculator, developed using speculative decoding and paged attention, has been made available on the Hugging Face platform for others to utilize in their LLMs. Additionally, IBM plans to implement these optimization techniques across all models on its WatsonX platform for enterprise AI, promising improved cost performance and user experiences across various applications.
Overall, the integration of speculative decoding and paged attention represents a major breakthrough in accelerating AI inference and optimizing memory usage in large language models. By enhancing the efficiency and speed of LLMs, these techniques have the potential to revolutionize customer service chatbots and various other AI applications, making interactions with AI systems more seamless and responsive for users.
Article Source
https://research.ibm.com/blog/speculative-decoding
