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LLM on K8s Panel // LLMs in Conference in Production Conference Part II

Sep 8, 2023
33:43
Snipd AI
In this podcast, Manjot Pahwa, Rahul Parundekar, and Patrick Barker discuss the integration of Kubernetes and large language models (LLMs), the challenges of using Kubernetes for data scientists, and the considerations for hosting LMM applications in production. They also explore the abstraction of LLMs on Kubernetes, the cost considerations, and the pros and cons of using Kubernetes for LLM training versus inferencing. Additionally, they touch on using Kubernetes for real-time online inferences and the availability of abstractions like Metaplow.
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Podcast summary created with Snipd AI

Quick takeaways

  • Kubernetes offers scalability and reliability for ML workloads, but challenges arise with large language models due to size and startup times.
  • Platforms that automate LLM deployment on Kubernetes can simplify the process for data scientists, but robust platforms are recommended for complex deployments and iterative improvements.

Deep dives

Kubernetes as a reliable and scalable workload orchestrator

Kubernetes was designed to provide scalability and reliability for workloads, offering an abstraction layer and orchestration capabilities. While originally focused on traditional workloads, Kubernetes has been adapted for machine learning (ML) workloads. The platform abstracts away hardware, improving efficiency and scalability. However, challenges exist when dealing with large language models (LLMs) due to their size, such as longer startup times and potential issues with node provisioning and networking. Service-oriented architectures and inferencing workloads can also benefit from Kubernetes' scalability and composability.

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