AI Revolution Powered by GPUs and High-Performance Object Storage

We are high-performance object storage

AI Storage Delivers Performance at Scale

RustFS accelerates AI/ML workloads by leveraging its distributed architecture and object storage capabilities. During model training, RustFS's distributed setup allows parallel data access and I/O operations, reducing latency and speeding up training times. For model inference, RustFS's high-throughput data access ensures rapid retrieval and deployment of data stored for AI models, enabling predictions with minimal latency. More importantly, RustFS's performance scales linearly from 100 TB to 100 PB and beyond. This optimizes end-to-end AI workflows, enhancing model development and serving, resulting in more efficient AI workloads and faster response times for applications.

Core of the AI Ecosystem

RustFS is the standard for S3-compatible object storage for AI workloads. This ubiquity means the entire AI/ML ecosystem integrates with RustFS. Don't take our word for it - enter your favorite framework and let Google provide the evidence.

Scale Required for Training and Inference

Enterprises continuously collect and store AI data that applications and large language models can use to retrain models for improved accuracy. RustFS's scalability allows organizations to scale their storage capacity on demand, ensuring smooth data access and high-performance computing essential for the success of AI/ML applications.

Resilient (Fault-Tolerant) AI Storage

RustFS allows organizations to store large amounts of data, including training datasets, models, and intermediate results in a fault-tolerant manner. This resilience is crucial for ML and AI storage as it ensures data remains accessible even in cases of hardware failures or system crashes. With RustFS's distributed architecture and data replication capabilities, AI/ML workflows can run seamlessly and continue to provide accurate insights and predictions, enhancing the overall reliability of AI-driven applications.

Reliable (Always On) Storage for AI Workloads

RustFS's active-active replication capabilities support simultaneous access across multiple geographically distributed clusters. This is important for AI/ML as it enhances data availability and performance. AI/ML workloads often involve globally collaborative teams and require low-latency access to data stored for AI model training and inference - ensuring data can be accessed from the nearest cluster location, reducing latency. Additionally, it provides failover capabilities, ensuring uninterrupted access to data even during cluster failures, which is crucial for maintaining reliability and continuity of AI/ML processes.

Storage Solutions for Large Language Models

RustFS can seamlessly integrate with Large Language Models (LLMs) as a reliable and scalable storage solution for the massive data required by such models. Organizations can use RustFS storage for pre-trained LLMs, fine-tuning datasets, and other artifacts. This ensures easy access and retrieval during model training and model serving. RustFS's distributed nature allows parallel data access, reducing data transfer bottlenecks and accelerating LLM training and inference, enabling data scientists and developers to fully leverage the potential of large language models for natural language processing tasks.

Contextual Storage for Retrieval Augmented Generation

RustFS can serve as a high-performance object storage backend for AI models for Retrieval Augmented Generation (RAG) and data. In RAG setups, RustFS can store corpora used to create domain-specific responses from Large Language Models (LLMs). AI-powered applications can access the corpus and the result is more contextually relevant and accurate responses for natural language generation tasks, improving the overall quality of generated content.

Cloud as Operating Model - Starting with S3

RustFS adheres to cloud operating models - containerization, orchestration, automation, APIs, and S3 compatibility. This allows cross-cloud and cross-storage type data storage and access by providing a unified interface for storing and accessing data. Since most AI/ML frameworks and applications are designed to work with S3 APIs, having the best compatibility in the industry is crucial. With over 1.3 billion Docker pulls - no object storage has more developer and application validation of its compatibility - 24/7/365. This compatibility ensures AI workloads can access and leverage data stored in RustFS object storage regardless of the underlying cloud infrastructure, facilitating flexible and agnostic data management and processing approaches across different cloud environments.

Edge AI Storage

At the edge, network latency, data loss, and software bloat degrade performance. RustFS is the world's fastest object storage with a binary under 100 MB that can be deployed on any hardware. Additionally, features like RustFS Bucket Notifications and Object Lambda make it easy to build systems that can immediately run inference across newly introduced data. Whether it's airborne object detection on high-altitude drones or traffic trajectory prediction for autonomous vehicles, RustFS's AI storage enables mission-critical applications to store and use their data in a fast, fault-tolerant, and simple manner.

Lifecycle Management for ML/AI Workloads

Modern AI/ML workloads require sophisticated lifecycle management. RustFS's lifecycle management capabilities automate data management tasks, optimizing storage efficiency and reducing operational overhead. With lifecycle policies, organizations can automatically move infrequently accessed AI data to lower-cost storage tiers, freeing up valuable resources for more critical and active workloads. These capabilities ensure AI/ML practitioners can focus on model training and development while RustFS intelligently manages data, enhancing overall workflow performance and cost-effectiveness. Additionally, lifecycle management layers ensure AI/ML datasets comply with regulatory requirements by enforcing retention and deletion policy requirements.

Object Retention for AI/ML Workflows

Few workloads depend more on when things happened than AI/ML. This is addressed through advanced object retention capabilities that ensure the integrity and compliance of stored data over time. By implementing retention policies, RustFS can help organizations maintain data consistency for AI/ML models and datasets, preventing accidental or unauthorized deletions or modifications. This feature is crucial for data governance, regulatory compliance, and reproducibility of AI/ML experiments, as it guarantees that critical data remains accessible and immutable for specific durations, supporting precise model training and analysis.

Data Protection for Core AI Datasets

RustFS protects data through a number of different capabilities. It supports erasure coding and site replication, ensuring data redundancy and fault tolerance to prevent hardware failures or data corruption. RustFS also allows data encryption at rest and in transit, protecting data from unauthorized access. Additionally, RustFS's support for Identity and Access Management (IAM) enables organizations to control access to their data stored for AI workloads, ensuring only authorized users or applications can access and modify data. These comprehensive data protection mechanisms provided by RustFS help maintain the integrity, availability, and confidentiality of AI datasets throughout their lifecycle.