You know Kubernetes 😃
but do you know how to run a simple AI app on it?
Hear real engineers explain the basic tech stack you need to manage AI workloads today 😎
What you will learn:
You will gain a clear, actionable roadmap for deploying LLMs.
You will know how to reduce GPU cloud costs & security and isolation trade-offs for production MLOps.
You will know the non-negotiable prerequisites to make Kubernetes see a GPU.
Link to the podcast: (Read below the video to find what is covered in the podcast)
We also talked about key challenges.
Like: GPUs and Kubernetes don’t talk to each other easily yet.
Discover the crucial tools and plugins you must install to make your cluster see and use that powerful hardware.
What else is covered in the podcast:
Understanding GPU Workloads in Kubernetes
A core takeaway is that Kubernetes does not natively support scheduling GPU workloads in the same way it handles CPU-based workloads
You cannot simply define millicores for a GPU.
How GPU Resource Allocation Works
Learn why Kubernetes defaults to allocating entire GPUs to a single pod, leading to massive resource waste, and how to prevent it.
Know the non-negotiable prerequisites—Nvidia drivers and the device plugin—required before your K8s cluster can even see a GPU.
Solve Real-World Operational Challenges
Identify an important challenge in training models across multiple nodes.
Achieving high-speed network bandwidth, and how it directly impacts model training latency.
A proven strategy for scaling GPU-based pods, and implementing pre-launching techniques to maintain low latency.
Learn the MLOps Career Roadmap
A practical, approach to go from zero to successfully deploying a Large Language Model (LLM) inside a Kubernetes cluster.
Understand the severe security risk of memory sharing
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Thank you. Be happy and Keep Smiling 😁
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