Presented by F5As enterprises pour billions into GPU infrastructure for AI workloads, many are discovering that their expensive compute resources sit idle far more than expected. The culprit isn't the hardware. It’s the often-invisible data delivery layer between storage and compute that's starving GPUs of the information they need."While people are focusing their attention, justifiably so, on GPUs, because they're very significant investments, those are rarely the limiting factor," says Mark Menger, solutions architect at F5. "They're capable of more work. They're waiting on data."AI performance increasingly depends on an independent, programmable control point between AI frameworks and object storage — one that most enterprises haven’t d [...]
DoorDash and Wing have announced a new partnership that will allow users in metro Atlanta to have food delivered by drone. Besides working with DoorDash in select regions of Virginia, North Carolina a [...]
ScaleOps has expanded its cloud resource management platform with a new product aimed at enterprises operating self-hosted large language models (LLMs) and GPU-based AI applications. The AI Infra Prod [...]
A viral Reddit post purportedly from an employee of a "major food delivery app" may actually be an AI-generated hoax, The Verge reports. The post itself, and an image of an employee ID card [...]