The Generative AI (GenAI) landscape is rapidly evolving, and the cloud-native GenAI stack is the foundation for building, deploying, and scaling these powerful AI models. This blog dives into the various layers of the cloud-native GenAI stack, exploring the underlying technologies and considerations at each level.
- Chip/GPU Infrastructure Layer:
The foundation of the stack is the hardware layer, comprised of physical compute resources. This layer is crucial for providing the processing power required for training and running GenAI models. Here’s a breakdown of some key vendors:
- GPUs (Graphics Processing Units): Leading vendors like Nvidia offer powerful GPUs specifically designed for AI workloads, due to their parallel processing capabilities. These are often the preferred choice for training large GenAI models.
- CPUs (Central Processing Units): While not as performant as GPUs for specific AI tasks, CPUs from vendors like Intel and AMD can still be suitable for smaller models or specific functionalities within the GenAI stack.
- TPUs (Tensor Processing Units): These specialized AI accelerators, offered by companies like Google (TPUs) and Amazon (Trainium), are designed for high-performance machine learning tasks and can be a cost-effective option for certain GenAI workloads.
- NPUs (Neural Processing Units): Emerging NPU offerings from companies like Cambricon are specifically designed for neural network processing and may play a larger role in the future of GenAI hardware.
- Hyperscaler Layer:
Cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer the infrastructure and services needed to build and deploy cloud-native GenAI applications. These hyperscalers provide:
- Virtual Machines (VMs): Traditional VMs offer a familiar environment for deploying GenAI workloads, but may not be the most cost-effective option for large-scale deployments.
- Container Orchestration Platforms: Containerization technologies like Docker and Kubernetes enable packaging and managing microservices within the GenAI application. Kubernetes is a popular choice for its scalability and orchestration capabilities. Alternative platforms like Nomad and Mesos are also used in some cloud-native deployments.
- Storage Solutions: Scalable and reliable storage solutions are crucial for housing the massive datasets required for training GenAI models. Cloud providers offer various storage options like object storage and block storage to cater to different needs.
- Platform Layer for Orchestration:
This layer manages the lifecycle of containerized GenAI workloads. Kubernetes is a dominant player here, offering features like:
- Automated deployments and scaling: Kubernetes automates deploying and scaling containerized applications, ensuring efficient resource utilization for GenAI workloads.
- Self-healing capabilities: In case of failures, Kubernetes can automatically restart containers, promoting application resilience.
- Resource management: Kubernetes manages container resource allocation, ensuring optimal resource utilization for GenAI tasks.
- ML Lifecycle for LLM (Large Language Models):
This layer focuses on the various stages involved in building and deploying LLMs, a prominent type of GenAI model. Here’s a breakdown of the key stages:
- Data Preparation: Large volumes of text data are required for training LLMs. This stage involves data collection, cleaning, and pre-processing to ensure the quality of the training data.
- Model Preparation: The chosen LLM architecture is defined, and the model is prepared for training. This may involve selecting pre-trained models for fine-tuning or defining the model architecture from scratch.
- Model Training: The prepared LLM model is trained on the pre-processed data using powerful compute resources from the underlying hardware layer. Distributed training techniques are often employed to manage the massive datasets and complex models.
- Model Serving: Once trained, the LLM model is deployed for serving requests. This involves optimizing the model for low latency and efficient inference to handle real-time use cases.
- Workload Layer with Predictive/Generative Applications:
This topmost layer comprises the GenAI applications that leverage the trained models. These applications can be broadly categorized into two types:
- Predictive AI Applications: These applications use trained models to analyze data and predict future outcomes or identify patterns. This could involve tasks like sentiment analysis, text summarization, or anomaly detection.
- Generative AI Applications: These applications leverage trained models to generate entirely new content, such as text, code, or images. LLMs are a prime example of generative AI, capable of generating human-quality text formats like poems, code, scripts, musical pieces, email, letters, etc.
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