About Deploying Multicloud Distributed AI Workloads by Using Oracle Interconnect for Google Cloud

Training Large Language Models (LLM) can require a large amount of GPUs from multiple cloud providers in a region. This design solution introduces a multicloud approach to running LLM training and inference on Oracle Cloud Infrastructure (OCI) AI Infrastructure on demand by using Oracle Interconnect for Google Cloud with the application front end running on Google Kubernetes Engine (GKE).

OCI AI Cluster offers a robust platform for training large language models. These models, capable of generating human-quality text, translation, and code, require immense computational power and vast amounts of data. OCI AI Cluster provides the necessary infrastructure with high-performance computing resources and optimized networking to accelerate LLM training. Dedicated AI clusters are compute resources that you can use to fine-tune custom models or to host endpoints for the pre-trained base models and custom models in OCI Generative AI. The clusters are dedicated to your models and not shared with users in other tenancies.

About Generative AI and Google Kubernetes Engine

This solution leverages Oracle Cloud's AI infrastructure for GPU-accelerated model training while using familiar Kubernetes orchestration tools.

Generative AI is a fully managed OCI service that provides a set of state-of-the-art, customizable LLMs that cover a wide range of use cases, including chat, text generation, summarization, and creating text embeddings. You can use the playground to try out the ready-to-use pre-trained models or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters.

A GKE cluster consists of a control plane and worker machines called nodes. The control plane and nodes make up the Kubernetes cluster orchestration system. GKE Autopilot manages the entire underlying infrastructure of clusters, including the control plane, nodes, and all system components. If you use GKE Standard mode, GKE manages the control plane and system components, and you manage the nodes.

About the Benefits of this Architecture

Key benefits of using OCI AI Cluster for LLM training include:

• Scalability: Easily adjust compute resources to match training demands.
• Performance: Leverage high-performance networking and GPU-accelerated compute instances.
• Cost-efficiency: Optimize resource utilization and only pay for what you use.
• Security: Exploit Oracle's robust security measures to protect sensitive data.
• Integration: Seamlessly integrate with other OCI services for data management and model deployment.

By harnessing the power of OCI AI Cluster, organizations can develop and deploy sophisticated LLMs to drive innovation and business value.

Understand the Steps Involved in Training an LLM on an OCI AI Cluster

The steps necessary to train an LLM on OCI AI Cluster are:

1. Set up the AI Cluster environment.
2. Prepare and preprocess training data.
3. Select and configure an LLM architecture.
4. Implement training pipeline and hyperparameter tuning.
5. Evaluate model performance and fine-tuning.