UNIXMENS Telegram 20441
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

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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

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