Keywords: AI storage, VAST Data, GPU optimization, data auditability, EU AI Act compliance, scalable AI workflows, high-performance storage, AI infrastructure.
You’re building something extraordinary. Maybe it’s a computer vision model that analyzes satellite images, or perhaps it’s a large language model designed to understand complex documents. Whatever your focus, there’s one thing your workload absolutely depends on: data, lots of it.
But here’s the problem: working with massive datasets isn’t easy. From storing them efficiently to making them accessible for your compute resources, there are countless ways things can go wrong. Storage systems might lag, costs might spiral, or, worse, your AI pipeline could grind to a halt because your GPUs are sitting idle, waiting for data.
If you’ve ever struggled with scaling your data infrastructure, you’re not alone. Let’s take a moment to break down some of the key challenges, and then see how Genesis Cloud, powered by VAST Data, makes them disappear.
The first challenge you encounter when scaling AI workloads is the sheer volume of data. Datasets grow at an astonishing rate. A single project that starts with a manageable terabyte can quickly explode into petabytes, especially if you’re working with high-resolution images, video streams, or time-series data. And the problem doesn’t end with storage space. It’s one thing to store massive datasets, it’s another to ensure they’re accessible to your compute resources with low latency and high throughput.
Fragmentation compounds the issue. If you’ve had to manage both structured and unstructured data, you know how frustrating it can be. You might store labels and annotations in a relational database while keeping raw data files like images and logs in a completely different system. That works fine, until it doesn’t. When your workflows demand seamless integration of these datasets, you find yourself stuck migrating, duplicating, or reorganizing data, which wastes both time and resources.
And then there’s cost. Storage that’s fast enough to keep up with modern GPUs is often priced out of reach. Balancing high-performance requirements with budget constraints becomes a daily struggle. Worse, if your storage can’t match the pace of your GPUs, you’re paying for compute resources that sit idle, waiting for data. It’s an unsustainable cycle.
These challenges are real, and they take time and focus away from what you care about: building great models. That’s why Genesis Cloud, in partnership with VAST Data, has introduced a storage solution that eliminates these pain points, giving you the tools to scale effortlessly.
VAST Data offers a revolutionary storage architecture designed to address the exact needs of modern AI workloads. Integrated seamlessly into the Genesis Cloud platform, this system combines the performance of flash storage, the cost efficiency of traditional systems, and unparalleled scalability.
At the heart of VAST Data’s system is its Disaggregated Shared Everything (DASE) architecture, which completely rethinks how data is stored and accessed. In traditional systems, compute and storage are tightly coupled, which creates bottlenecks as you scale. VAST separates these functions into CNodes (Compute Nodes) and DBoxes (Data Boxes), allowing each component to scale independently based on your workload’s needs.
As seen in the diagram above, CNodes act as the system’s brains. They handle metadata, manage storage operations, and ensure data is delivered quickly and efficiently to the compute resources. For example, if your GPU instances request specific datasets, CNodes manage the lookup and coordinate the transfer seamlessly. DBoxes provide the physical storage. These are high-performance storage units housing flash SSDs and Storage Class Memory (SCM). SCM acts as a buffer for faster reads and writes, while the SSDs store the bulk of your data, ensuring high throughput and durability.
Together, CNodes and DBoxes create a system where data is always accessible with minimal latency, no matter how large or complex your workload. This separation of compute and storage ensures you can scale storage capacity and performance independently, eliminating bottlenecks that traditional systems often face.
But what really sets VAST apart is its unified namespace. Whether your data is structured (like tables and annotations) or unstructured (like videos and images), it’s stored in a single, cohesive system. There’s no need to manage multiple silos or worry about compatibility—everything is accessible through familiar protocols like NFS for direct file access or S3-compatible storage for cloud-native workflows.
This unified approach simplifies your workflows and eliminates the inefficiencies of fragmented storage. Imagine training a computer vision model. Your raw image data, metadata, and annotations are all in the same storage system, ready to be accessed directly by your GPUs without any migration or duplication. It’s seamless, and it just works!
When you store your datasets in VAST and run your compute workloads on Genesis Cloud, you’re leveraging a system built to eliminate bottlenecks and maximize efficiency. Imagine you’re training a natural language processing model with a dataset consisting of terabytes of text data. Using the NFS protocol, you mount the VAST storage directly to your GPU instance in Genesis Cloud. Your training script accesses the data as if it were stored locally, while behind the scenes, the high-speed NVMe-over-Fabrics (NVMe-oF) protocol ensures the data flows seamlessly and rapidly to your GPUs.
As training progresses, the system dynamically scales to handle throughput demands, ensuring that your GPUs stay busy processing data instead of sitting idle waiting for it. This drastically reduces training cycles, enabling quicker iterations and faster results. And when your work is complete, VAST simplifies the next steps: archive your outputs to S3-compatible storage for long-term purposes, share them with your team, or prepare them for deployment — all without workflow interruptions.
Best of all, the complexities of the underlying infrastructure, like managing CNodes and DBoxes, are completely abstracted away. Genesis Cloud takes care of provisioning scalable, high-performance storage, starting at $0.10 per GiB, so you can focus entirely on building and refining your models. Whether you’re training LLMs or running real-time inference pipelines, we handle the storage so you can handle the innovation. It’s fast, efficient, and it scales effortlessly as your needs grow.
The integration of VAST Data into Genesis Cloud isn’t just about solving today’s storage challenges, it’s about preparing you for the future. With a system that combines performance, simplicity, and cost-efficiency, you can focus entirely on building models, not managing infrastructure.
The Genesis Cloud team 🚀
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