# Quobyte: Storage Architected for AI™ > > Quobyte is a software-defined parallel file system for AI, HPC, and enterprise workloads. Founded by ex-Googlers, Quobyte applies hyperscaler design principles to deliver linear scale-out performance, software-based resiliency, and simple operations from small clusters to exabyte scale. It runs on standard x86 and ARM Linux servers and unifies NVMe, SSD, and HDD in one system with policy-based data placement. Applications can access the same data through native Linux, Windows, and macOS clients, as well as NFS, S3, HDFS, and MPI-IO. Quobyte can be deployed on-prem, in public clouds, at the edge, in hybrid environments, or directly on GPU nodes as GPU Converged Storage. > ## Product - [Product Overview](https://www.quobyte.com/product/): Overview of Quobyte's parallel file system architecture, AI-ready performance, resiliency, hybrid storage, multi-tenancy, observability, and deployment options. - [AI-Ready Performance](https://www.quobyte.com/product/ai-ready-performance/): Parallel storage architecture with linear scale-out of throughput, IOPS, and metadata performance for AI ingest, preprocessing, training, fine-tuning, checkpointing, and inference. - [Hybrid Storage](https://www.quobyte.com/hybrid-storage/): NVMe, SSD, and HDD in one storage system with policy-based placement and automated data movement, combining flash performance with disk economics. - [GPU Converged Storage](https://www.quobyte.com/product/gpu-converged-storage/): Runs Quobyte directly on GPU nodes and uses available CPU, RAM, and local NVMe to provide high-performance storage while reducing the need for separate storage hardware. - [Bulletproof Resiliency](https://www.quobyte.com/product/bulletproof-resiliency/): Software-based fault tolerance designed so drives, servers, racks, and other infrastructure components can fail without disrupting storage availability. - [Smartphone Simplicity](https://www.quobyte.com/product/smartphone-simplicity/): Flat, single-layer architecture running on standard Linux, servers, and networking, designed to simplify deployment, scaling, maintenance, and day-to-day operations. - [Software Defined](https://www.quobyte.com/product/software-defined/): Hardware-independent storage software for standard x86 and ARM servers, allowing organizations to choose, mix, reuse, and refresh hardware without being tied to proprietary storage appliances. - [Native Multi-Tenancy](https://www.quobyte.com/product/native-multi-tenancy/): Secure tenant isolation across namespaces, users, groups, volumes, quotas, authentication, management, and optional dedicated hardware resources. - [Observability](https://www.quobyte.com/product/observability/): Full-stack visibility from applications to storage devices, including file-level performance analytics, workload monitoring, alerts, Prometheus integration, and Grafana dashboards. - [Full Feature Matrix](https://www.quobyte.com/editions-features/): Detailed list of Quobyte features including data protection, security, protocols, data management, multi-cluster capabilities, Kubernetes integration, and management tools. - [Technology Whitepaper](https://www.quobyte.com/product/whitepaper/): Detailed technical description of Quobyte architecture, clients, services, replication, erasure coding, policy engine, security, caching, data management, and operations. ## AI and Inference - [Storage for Artificial Intelligence](https://www.quobyte.com/storage-for/machine-learning-and-ai/): Parallel storage for the complete AI pipeline, including data ingest, preprocessing, training, fine-tuning, checkpointing, model loading, and inference. - [GPU Converged Storage](https://www.quobyte.com/product/gpu-converged-storage/): GPU-converged architecture that turns local CPU and NVMe resources across GPU nodes into scalable, resilient storage for AI training and inference. - [KV Cache Offloading for Agentic LLMs](https://www.quobyte.com/blog/kv-cache-offloading-for-agentic-llms-why-multi-turn-inference-needs-gpu-converged-storage/): Explains how KV cache reloads affect multi-turn agentic inference and why storage throughput and latency matter when conversations and agent workflows resume. ## Solutions - [Artificial Intelligence](https://www.quobyte.com/storage-for/machine-learning-and-ai/): High-performance parallel storage designed to keep GPUs supplied with data across AI training, fine-tuning, checkpointing, and inference workloads. - [High-Performance Computing](https://www.quobyte.com/storage-for/high-performance-computing-hpc/): Parallel file system storage for large-scale HPC workloads with RDMA support, scalable throughput, metadata performance, and support for mixed file sizes. - [Financial Services](https://www.quobyte.com/storage-for/financial-services/): Parallel storage for quantitative research, backtesting, market data analytics, and other compute-intensive financial workloads. - [Life Science and Bio IT](https://www.quobyte.com/storage-for/life-science-bio-it/): High-performance storage for genomics, cryo-EM, medical imaging, and other scientific workloads involving large datasets and mixed file sizes. - [Kubernetes and Containers](https://www.quobyte.com/storage-for/kubernetes-and-containers/): Shared persistent storage for Kubernetes with a CSI driver, ReadWriteMany volumes, multi-tenancy, access controls, quotas, snapshots, and multiple storage classes. ## Architecture and Data Management - [Hybrid Storage](https://www.quobyte.com/hybrid-storage/): Combines flash and HDD within one architecture instead of requiring separate performance and capacity storage systems. - [Software Defined](https://www.quobyte.com/product/software-defined/): Explains Quobyte's hardware-independent architecture and ability to run across x86, ARM, on-premises infrastructure, cloud instances, edge systems, and GPU nodes. - [Bulletproof Resiliency](https://www.quobyte.com/product/bulletproof-resiliency/): Describes Quobyte's software-only approach to fault tolerance, automatic failover, and non-disruptive operations. - [Observability](https://www.quobyte.com/product/observability/): Covers application-to-drive monitoring, file-level analytics, Prometheus metrics, Grafana dashboards, real-time reports, and the Quobyte File Query Engine. - [Full Feature Matrix](https://www.quobyte.com/editions-features/): Reference for replication, erasure coding, encryption, ACLs, cloud tiering, multi-cluster synchronization, snapshots, QoS, policy-based data management, S3, NFS, HDFS, MPI-IO, and Kubernetes support. ## Comparisons - [Alternative to Qumulo](https://www.quobyte.com/alternative-to-qumulo/): Comparison of Quobyte and Qumulo across architecture, performance scaling, hardware flexibility, protocols, and operations. - [Alternative to Dell EMC PowerScale / Isilon](https://www.quobyte.com/alternative-to-dell-emc-powerscale-isilon/): Comparison covering scale-out architecture, hardware model, performance, multi-tenancy, management, and deployment flexibility. - [Alternative to IBM Spectrum Scale / GPFS](https://www.quobyte.com/alternative-to-ibm-spectrumscale-gpfs/): Feature comparison covering architecture, scalability, hardware flexibility, security, operations, Kubernetes, and data management. - [BeeGFS Alternative and Comparison](https://www.quobyte.com/blog/beegfs-alternative-and-comparison/): Comparison of Quobyte and BeeGFS across fault tolerance, erasure coding, operations, hardware requirements, multi-tenancy, and data management. - [Alternative to WEKA](https://www.quobyte.com/alternative-to-weka/): Comparison of Quobyte and WEKA across architecture, hardware requirements, flash and HDD support, resiliency, operations, multi-tenancy, and deployment flexibility. - [Alternative to VAST Data](https://www.quobyte.com/alternative-to-vast/): Comparison of Quobyte and VAST Data across storage architecture, hardware, protocols, scaling, data management, security, and operations. ## Customer Stories - [Zoox](https://www.quobyte.com/resources/zoox-bringing-driverless-transportation-to-the-masses/): Zoox scaled autonomous vehicle AI storage from 2 PB to more than 30 PB using Quobyte hybrid-tier, high-performance storage. - [Siemens Healthineers](https://www.quobyte.com/resources/siemens-healthineers-digital-technology-and-innovation/): Siemens Healthineers uses Quobyte for a multi-petabyte AI data platform supporting more than 1,600 AI experiments per day and work behind 60+ FDA-approved AI models. - [Yahoo! Japan](https://www.quobyte.com/resources/yahoo-japan/): Yahoo! Japan selected Quobyte as a software-defined storage platform for large-scale data center infrastructure. - [OHSU](https://www.quobyte.com/resources/ohsu/): Oregon Health & Science University uses Quobyte for high-performance cryo-electron microscopy data processing and research workflows. ## Resources - [Quobyte Technology Whitepaper](https://www.quobyte.com/product/whitepaper/): In-depth technical reference for the Quobyte parallel distributed file system and its architecture. - [Quobyte Blog](https://www.quobyte.com/blog/): Technical articles on AI infrastructure, storage architecture, HPC, Kubernetes, data management, and Quobyte technology. - [Customer Stories](https://www.quobyte.com/company/our-customers/): Customer deployments across AI, autonomous driving, healthcare, research, life sciences, HPC, and enterprise infrastructure. - [Tutorials](https://www.quobyte.com/tutorials/): Technical guides for deploying and integrating Quobyte with Kubernetes, Hadoop, Spark, and other environments. > ## About Quobyte - [About Quobyte](https://www.quobyte.com/company/): Quobyte was founded by ex-Googlers Felix Hupfeld and Björn Kolbeck to bring hyperscaler design principles to enterprise storage. - [Contact Quobyte](https://www.quobyte.com/contact/): Contact Quobyte for product information, architecture discussions, demonstrations, and evaluations. For more information, visit https://www.quobyte.com/.