Academy and Foundation unixmens | Your skills, Your future

As enterprises scale large language models (LLMs) into production, site reliability engineers (SREs) and platform operators face a new set of challenges. Traditional application metrics—CPU usage, request throughput, memory consumption—are no longer enough. With LLMs, reliability and efficacy are defined by entirely new dynamics—token-level performance, cache efficiency, and inference pipeline latency.This article explores how llm-d, an open source project co-developed with the leading AI vendors (Red Hat, Google, IBM, etc.) and integrated into Red Hat OpenShift AI 3.0, redefines observa

via Red Hat Blog https://ift.tt/MxqW7CO

Redhat

Redefining LLM observability with llm-d in Red Hat OpenShift AI 3.0

Explore how llm-d, an open source project co-developed with leading AI vendors, redefines observability for LLM workloads in Red Hat OpenShift AI 3.0. Learn about new service level objectives, cache hit rate, and more.

www.tgoop.com/unixmens/20441

67 viewsOct 22 at 14:29

As enterprises scale large language models (LLMs) into production, site reliability engineers (SREs) and platform operators face a new set of challenges. Traditional application metrics—CPU usage, request throughput, memory consumption—are no longer enough. With LLMs, reliability and efficacy are defined by entirely new dynamics—token-level performance, cache efficiency, and inference pipeline latency.This article explores how llm-d, an open source project co-developed with the leading AI vendors (Red Hat, Google, IBM, etc.) and integrated into Red Hat OpenShift AI 3.0, redefines observa

via Red Hat Blog https://ift.tt/MxqW7CO

BY Academy and Foundation unixmens | Your skills, Your future