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At this point, the transformative potential of a large language model (LLM) is clear, but efficiently deploying these powerful models in production can be challenging.This challenge is not new. In a recent episode of the Technically Speaking podcast, Chris Wright spoke with Nick Hill, a principal software engineer at Red Hat who worked on the commercialization of the original IBM Watson "Jeopardy!" system years ago. Hill noted that these early efforts focused on optimizing Watson down from a room full of servers to a single machine, establishing that systems-level engineering is key to making

via Red Hat Blog https://ift.tt/SRfJ3tu

Redhat

Accelerate AI inference with vLLM

At this point the transformative potential of a large language model LLM is clear but efficiently deploying these powerful models in production can be challenging

www.tgoop.com/unixmens/20309

178 viewsSep 19 at 15:29

At this point, the transformative potential of a large language model (LLM) is clear, but efficiently deploying these powerful models in production can be challenging.This challenge is not new. In a recent episode of the Technically Speaking podcast, Chris Wright spoke with Nick Hill, a principal software engineer at Red Hat who worked on the commercialization of the original IBM Watson "Jeopardy!" system years ago. Hill noted that these early efforts focused on optimizing Watson down from a room full of servers to a single machine, establishing that systems-level engineering is key to making

via Red Hat Blog https://ift.tt/SRfJ3tu

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