The AI era demands a simple infrastructure strategy that prioritizes scalability, performance and cost efficiency in managing AI data pipelines. A key challenge is supporting large language model (LLM) training, which requires massive data, compute and storage resources. Efficient training relies on the continuous feeding of large data sets and the storage of model parameters, intermediate results and checkpoints. Above all, the infrastructure strategy must ensure that the AI resources are scalable, reliable and cost-efficient.Scaling AI Training InfrastructureAs models grow, so do the demands on AI storage, making efficient data management crucial throughout the training pipeline. AI training, especially for large models, involves distributing the workload across multiple nodes to speed up the process, which requires access to the same data set. Many organizations use parallel file systems to distribute data across storage nodes. However, this approach can lead to infrastructure sprawl, increased costs and operational complexity — challenges that directly affect DevOps teams.Multiattach Block Storage: A Simple SolutionA more streamlined and cost-effective solution involves using a local file system mounted as read-only across multiple nodes. This is achieved by storing the data set on a high-performance block storage system with multiattach capabilities. The block storage volume is attached to multiple nodes simultaneously and mounted as a local file system in read-only mode.Benefits:Reduces the complexity of managing large, distributed file systems.Easily scale storage capacity and performance by leveraging the multiattach capabilities of block storage.Optimizes resource utilization and reduces infrastructure costs.Enables Infrastructure as Code practices for provisioning and managing storage resources.This approach provides a more scalable and efficient method for handling AI training storage, aligning with modern infrastructure demands and DevOps principles.Inference: The Next FrontierWhile AI training is often centralized, inference is frequently deployed across diverse environments. Modern inference involves complex multistep processes, increasing the demand for memory, compute and fast data access.Challenges in inference:Deploying and managing inference at scale requires robust infrastructure that can handle fluctuating workloads and low-latency requirements.Balancing performance with cost efficiency is critical, avoiding over-provisioning while meeting real-time demands.Inference workloads are often unpredictable, requiring automation and monitoring to handle spikes in demand.Key-Value Caching and Storage OptimizationInference with LLMs relies heavily on key-value (KV) caches to store intermediate results, reducing redundant computations and improving resource efficiency. However, storing KV caches in GPU or host memory has limitations in capacity and scalability.Storage Solutions for Scalable InferenceTo overcome these limitations, extending KV cache storage to high-performance storage solutions is essential.NVMe flash: Offers a cost-effective, high-capacity tier for KV cache storage, alleviating GPU and memory constraints. Infrastructure teams can leverage NVMe over TCP, which is NVME over standard Ethernet, to build scalable and performant inference infrastructure.Disaggregated KV cache architecture: Distributing the KV cache across NVMe flash and making it accessible from any GPU server is crucial for optimal performance.Cache tiering: Intelligent management of cache hierarchies across GPU RAM, host RAM, local SSDs and disaggregated SSDs is key.LLM Distillation and Efficient DeploymentLLM distillation, which involves creating smaller, faster models, is an essential trend for efficient deployment. Offloading KV cache to remote storage can further free up local memory, enabling distilled models to handle longer contexts and scale more effectively. This is particularly relevant for infrastructure teams deploying AI at the edge or in cloud environments with resource constraints.By focusing on these storage optimization strategies and adopting DevOps practices, organizations can build scalable, cost-effective and energy-efficient AI infrastructure to support both training and inference workloads.If you are interested to learn more about infrastructure optimization for AI and machine learning workloads, read our blog, “Stop Wasting Money on AI Storage: A Smarter, Leaner Approach.”The post Leaner, More Efficient Storage Infrastructure for the AI Era appeared first on The New Stack.