As GenAI and AI continue to transform businesses, scaling these workloads requires optimized underlying infrastructure. A multi-cloud architecture allows organizations to leverage different cloud services to meet diverse workload demands while maximizing efficiency, reducing costs, and avoiding vendor lock-in. However, achieving a multi-cloud vision can be challenging.
In this webinar, Tarik will share how an agonistic data layer, like Alluxio, allows you to embrace the separation of storage from compute and simplify the adoption of multi-cloud for AI.
- Learn why leveraging multiple cloud providers is critical for balancing performance, scalability, and cost of your AI platform
- Discover how an agnostic data layer like Alluxio provides seamless data access in multi-cloud that bridges storage and compute without data replication
- Gain insights into real-world examples and best practices for deploying AI across on-prem, hybrid, and multi-cloud environments
As GenAI and AI continue to transform businesses, scaling these workloads requires optimized underlying infrastructure. A multi-cloud architecture allows organizations to leverage different cloud services to meet diverse workload demands while maximizing efficiency, reducing costs, and avoiding vendor lock-in. However, achieving a multi-cloud vision can be challenging.
In this webinar, Tarik will share how an agonistic data layer, like Alluxio, allows you to embrace the separation of storage from compute and simplify the adoption of multi-cloud for AI.
- Learn why leveraging multiple cloud providers is critical for balancing performance, scalability, and cost of your AI platform
- Discover how an agnostic data layer like Alluxio provides seamless data access in multi-cloud that bridges storage and compute without data replication
- Gain insights into real-world examples and best practices for deploying AI across on-prem, hybrid, and multi-cloud environments
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Videos
TorchTitan is a proof-of-concept for Large-scale LLM training using native PyTorch. It is a repo that showcases PyTorch's latest distributed training features in a clean, minimal codebase.
In this talk, Tianyu will share TorchTitan’s design and optimizations for the Llama 3.1 family of LLMs, spanning 8 billion to 405 billion parameters, and showcase its performance, composability, and scalability.
As large-scale machine learning becomes increasingly GPU-centric, modern high-performance hardware like NVMe storage and RDMA networks (InfiniBand or specialized NICs) are becoming more widespread. To fully leverage these resources, it’s crucial to build a balanced architecture that avoids GPU underutilization. In this talk, we will explore various strategies to address this challenge by effectively utilizing these advanced hardware components. Specifically, we will present experimental results from building a Kubernetes-native distributed caching layer, utilizing NVMe storage and high-speed RDMA networks to optimize data access for PyTorch training.