Storage Architected for AI™
The fastest AI-ready performance, bulletproof resiliency, and smartphone-like simplicity at any scale, on-prem or in the cloud. With Quobyte, small teams can deploy big AI projects with maximum return-on-investment.
The Hyperscaler Blueprint for AI Storage
AI and Large Language Model (LLM) training have pushed IT infrastructure beyond limits. Today, successful projects require a massive, continuous stream of data to keep expensive GPUs busy – a level of performance most enterprises have never had to manage.
Traditional enterprise storage cannot deliver the speed and scale needed to feed GPUs, while HPC storage kills projects with its complexity and frequent downtime.
Quobyte is a radically different approach to storage: Founded by two ex-Googlers, Quobyte brings the hyperscaler proven technology and operational simplicity directly to your enterprise. Quobyte is storage architected for AI™.
Fastest AI-Ready Performance
Quobyte’s hyperscaler-inspired architecture delivers massive, linearly scalable performance across all stages of the AI pipeline. Demonstrated with MLPerf® Storage benchmarks, it achieves more throughput with less hardware and lower power, ensuring maximum GPU utilization at any scale.
Bulletproof Resiliency
Quobyte is designed so anything can fail without ever stopping your workloads. With software-only fault tolerance at datacenter scale, it delivers hyperscaler-efficient operations and 100% availability with no exotic hardware, and no maintenance windows.
Smartphone Simplicity
Quobyte’s flat single-layer architecture is easy to deploy and run at massive scale. It runs on standard Linux, hardware, and networks with no kernel modules or exotic components, so small teams can manage exabytes of storage and adapt capacity in minutes.
Scalability at AI Scale
Quobyte scales linearly from a handful of servers to thousands, without disruption. It delivers predictable performance that scales linearly from terabytes to exabytes, ensuring AI pipelines run at full speed as your infrastructure grows on demand.
Software Defined, Not Hardware Confined
Quobyte is completely hardware-agnostic and built on the principle to never trust the underlying infrastructure. It runs on any x86 or ARM server, on-prem, in the cloud, at the edge, or in hybrid environments, freeing you from appliances and restrictive compatibility lists.
GPU Converged Storage
Quobyte can run directly on your GPU nodes, including Nvidia Grace Blackwell, to use local CPUs and NVMe drives. This converged approach lowers latency, power consumption, and cost while maintaining full resiliency in dynamic AI environments.
Hybrid Storage Without the Tradeoffs
Most hybrid storage is two systems joined by an API. Quobyte runs NVMe and HDD inside a single architecture, with placement governed by the policies you set and carried out automatically. Active AI training and inference get flash speed, while cold data and archives sit on disk at a fraction of the cost, with no archive to wait on and no tiers for your team to manage.
Open and Observable
Quobyte delivers end-to-end observability so you can act and optimize without guessing. As an open system, it exposes metrics from every component through APIs and integrates seamlessly with Prometheus and Grafana for real-time insight.
Multi-Tenancy
Quobyte is architected for secure multi-tenancy with strong isolation between users, teams, or projects. Policy-based data management provides the tools for isolating tenants in hardware, optimizing performance and cost, and full control at scale.
Security 360
Quobyte protects data at every layer with encryption in flight and at rest, fine-grained access controls, and comprehensive auditing. It delivers enterprise-grade security for sensitive AI and life science workloads without compromising performance.
Getting Started with Quobyte
Quobyte is Uniquely Architected for AI™
Quobyte delivers massive, scalable performance, paired with hyperscaler-inspired resiliency and operational simplicity, all on commodity hardware.
Successful Customers
"Data size for each deep learning job can be several Terabytes and copying it to scratch space is impractical. I needed to find a production-ready scalable storage solutions that could be accessed directly by AI training workloads at high speed without sacrificing reliability, maintainability, and fault-tolerance."
Talk to Us
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