Related resources:
- Crusoe AI Cloud Blog: “Block Storage for AI/ML Workloads, Powered by Lightbits“
Building AI and Generative AI (Gen-AI) cloud architectures? You are probably looking for a way to improve your overall performance and efficiency. When investigating AI cloud solutions, include high performance block storage for AI/ML in your architecture plan.
While large datasets typically reside in relatively slow storage systems (like S3/Object) optimized for capacity efficiency, various critical stages of AI pipelines demand high-speed and low-latency storage. This is where fast block storage solutions like NVMe SSDs or NVMe over Fabric (NVMe-oF) come into play, taking a key role in enhancing AI pipeline performance and efficiency. In this blog, we are going to discuss a few practical advantages and specific scenarios where NVMe block storage can notably improve your overall AI cloud infrastructure.
Block Storage for AI Accelerates Data Pipelines
Let’s have a look at few examples:
Streamlined Data Preprocessing: Data preprocessing is a phase where raw data is cleaned and transformed into a format that can be effectively utilized in machine learning models. When dealing with massive datasets, the speed at which data and parameters can be read and processed is crucial. By utilizing NVMe technology to store raw data, parameters, labels, etc., you will boost your infrastructure, accelerate processing, reduce your tail latency and increase the overall throughput. For instance, an image processing pipeline that normalizes and augments millions of images for a deep learning model can perform these tasks with higher efficiency and significantly faster when the intermediate data and tuning parameters are stored on local NVMe SSDs.
Boosting Model Training: During the training phase, data is frequently accessed, and model parameters (e.g. rate, batch size, dropout, layers, weights, etc.) are continuously adjusted. Utilizing fast NVMe storage during this stage can substantially reduce the time it takes to train models. Particularly in hyperparameter tuning, where multiple instances of a model are trained with varying parameters, having rapid access to training data and a fast medium to write updated parameters is vital. For instance, training a deep neural network for object detection or natural language processing can be expedited as the frequent read/write operations during backpropagation are performed with minimal latency.
Efficient Real-time Inference: In real-time AI, low latency is crucial for applications such as fraud detection, autonomous vehicles, or real-time translations. When an AI model needs to make instant predictions, the speed at which the model parameters are accessed is critical. Local NVMe storage ensures that the latency between the retrieval of model parameters and inference is minimal, which is extremely important for applications where decisions need to be made in fractions of a second.
Database Optimization: Many AI applications rely on databases to store and retrieve various parameters and tags (e.g., weights, biases, target variables, tracking steps, labels, etc.), as well as specific datasets. Whether the databases are used for storing training parameters and tags, or for managing data in real-time AI applications, ensuring peak performance is essential. By using NVMe devices, these databases can perform significantly faster queries and overall orchestration becomes highly efficient due to the low latency and high IOPS of NVMe technology. Consider a recommendation system that needs to query a database for user preferences and historical data – with an optimized database, it can deliver more accurate and prompt recommendations.
Scalability and Flexibility: AI models and datasets are not static; they are constantly growing and becoming more complex. As datasets grow and can no longer fit into local memory, the underlying storage infrastructure must scale as well. How can we solve this problem? The most scalable option would be NVMe-oF technology. NVMe-oF allows you to extend your storage capacity, with no performance or latency penalty and gives you a scalable high-performance, consistent low-latency storage infrastructure. You can also improve your overall efficiency by dynamically sharing storage resources over the network, reducing the number of hardware server configurations, simplifying the deployment, and even reducing power consumption.
Conclusion: Incorporating fast block storage for AI/Gen-AI using NVMe-oF technology is essential for building efficient and high-performance cloud architectures. From data preprocessing to real-time inference, the advantages of reduced latency, higher throughput, and scalability make NVMe-oF an excellent option for optimizing AI workflows. Investing in fast NVMe-oF storage is a practical step in ensuring that your AI cloud architecture is built for performance and future scalability.
