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What’s the most important factor in the performance and success of your AI model: architecture, hardware, or software? If you’ve been following the industry long enough, you know the answer is all of them.
It’s never just one thing that determines the success of your AI model for training or inference. It’s the culmination of many tiny details including networking, purpose-built software, and thoughtfully built infrastructure that results in a truly AI-optimized solution.
That’s why yesterday’s cloud infrastructure doesn’t meet the needs of your modern compute-intensive workloads. When you work with a generalized cloud, you get the same underlying infrastructure that powers a Wordpress site for training an AI model. Sure, these clouds have added abstractions to improve performance, but the base ingredients are the same.
When it comes to your AI ambitions, you need a high-performance computing platform that’s purpose-built for model training and inference from the ground up.
At Weights & Biases Fully Connected 2024, we discussed the importance of specialized infrastructure, what it looks like, and some of the essential software pieces you need for model training and inference. Take a look at the recording and see how all these elements—from architecture to hardware to software—come together to create a performant, reliable, and scalable solution for your AI infrastructure needs.
Inside this 18-minute video, we’ll cover topics like:
- Automating hardware validation and lifecycle platform
- Node lifecycle and health checks
- The power of data processing units (DPUs)
- Unified workload management with CoreWeave SUNK (Slurm on Kubernetes)
- Improving model deployment with CoreWeave Tensorizer
- Launch, a collaboration with our partners at Weights & Biases
For more information about any of the topics we discussed, please reach out to our team.
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