UltiHash - Lightning-fast object storage for AI

UltiHash

3.5 | 285 | 0
Type:
Website
Last Updated:
2025/08/16
Description:
UltiHash: Lightning-fast, S3-compatible object storage built for AI, reducing storage costs without compromising speed for inference, training, and RAG.
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object storage
data lakehouse

Overview of UltiHash

UltiHash: Lightning-Fast Object Storage for AI

What is UltiHash? UltiHash is a high-performance, S3-compatible object storage solution designed specifically for AI workloads. It offers both serverless and self-hosted options to cater to diverse needs.

Key Features:

  • Lightning-Fast Performance: Optimized for high throughput, ensuring GPUs and TPUs are fed at peak efficiency.
  • S3 Compatibility: Seamless integration with existing stacks using the S3-compatible API.
  • Kubernetes-Native: Built for teams running AI & data-intensive workloads at scale.
  • Binary-Level Deduplication: Reduces storage costs by detecting and eliminating duplicate data at the binary level.
  • Serverless Option: No clusters to manage, simplifying setup and scaling.
  • Self-Hosted Option: Full control over deployment and configuration, ideal for PB-scale workloads.

Use Cases:

  • Generative AI: Organizes and retrieves unstructured data for generative models, handling text, images, video, and audio without bottlenecks.
  • Model Training: Delivers high-throughput object storage to keep GPUs fully utilized and reduce storage costs.
  • Data Lakehouse: Unifies analytics and AI on one storage layer, managing structured and unstructured data together.

Benefits:

  • Lower TCO: Built-in deduplication and storage efficiency reduce costs.
  • Secure by Design: Features include erasure coding, access control, versioning, and SOC-2 Type II certification.
  • Flexible Deployment: Deploy on-prem or in a VPC with Kubernetes-native integration.

How to get started?

See what UltiHash can do with a test cluster. Follow the installation guide using Kubernetes for on-prem or cloud deployment.

UltiHash empowers developers and data scientists to build and scale AI applications efficiently. It's a solution that addresses the I/O bottlenecks and high storage costs commonly associated with AI workloads.

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