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**87% of AI workloads are sitting idle on GPUs right now** - yet companies keep buying more hardware. What if the problem isn't capacity, but how we're running AI on Kubernetes?
In today's Platform Engineering Playbook, we tackle the massive inefficiencies plaguing AI infrastructure at scale. You'll discover why traditional Kubernetes patterns break down with AI workloads, what's actually happening under the hood when you try to serve ML models in production, and concrete strategies to fix GPU utilization without throwing more money at the problem.
**What You'll Learn:**
**Timestamps:**
Whether you're scaling AI workloads or just trying to understand why your GPU bills keep growing while performance stays flat, this episode gives you the platform engineering perspective you need.
**Sources & References:**
#PlatformEngineering #DevOps #CloudNative #Kubernetes
By vibesre**87% of AI workloads are sitting idle on GPUs right now** - yet companies keep buying more hardware. What if the problem isn't capacity, but how we're running AI on Kubernetes?
In today's Platform Engineering Playbook, we tackle the massive inefficiencies plaguing AI infrastructure at scale. You'll discover why traditional Kubernetes patterns break down with AI workloads, what's actually happening under the hood when you try to serve ML models in production, and concrete strategies to fix GPU utilization without throwing more money at the problem.
**What You'll Learn:**
**Timestamps:**
Whether you're scaling AI workloads or just trying to understand why your GPU bills keep growing while performance stays flat, this episode gives you the platform engineering perspective you need.
**Sources & References:**
#PlatformEngineering #DevOps #CloudNative #Kubernetes