Fast Inference with Hassan El Mghari
Software Huddle
Optimizing Inference Speed in AI
This chapter explores the critical role of speed in inference engines used with AI and LLMs. It discusses innovative strategies for enhancing performance, including speculative decoding and the Together Kernels Collection, while also addressing the significance of fine-tuning and prompt engineering. The chapter concludes with insights into customer preferences for selecting machine learning models, emphasizing the use of a combination of open-source models for better application outcomes.
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