AI & Machine Learning Solutions

Compute power shouldn't sit idle waiting for data.
In AI and large-scale machine learning, data is fuel. Whether aggregating PB-scale raw data to compute centers or distributing trained model weights to the edge, FileBolt bridges data silos, ensuring GPU clusters run at full capacity.

1. The Challenge: Data Gravity

Large model training requires massive datasets. From autonomous driving logs to genomic sequencing, datasets have reached the Petabyte scale. Moving this data is often slower and more uncertain than the computation itself.

AI teams face major transfer bottlenecks:

Cross-DC Migration Issues: Training clusters are often in remote data centers with low power costs, while data collection is global. Uploading massive data across regions suffers from low bandwidth utilization.
High Cost of Interruption: If a 50TB dataset transfer fails midway without resume capability, it wastes days of time and leaves expensive GPU resources idle.
Privacy & Compliance: Training data involving faces, voice, or medical imaging is highly sensitive. Standard tools often fail to meet compliance requirements like GDPR.

2. Speed: Feeding High-Performance Clusters

FileBolt uses UDP-based optimization to maximize physical bandwidth usage, matching compute speed with data speed:

Saturate Leased Lines: Whether on 10Gbps or 100Gbps networks, FileBolt's multi-threaded concurrency elevates bandwidth utilization to over 98%.
Rapid Model Distribution: Distribute trained LLM weights to global inference nodes instantly using edge networks, shortening the model deployment cycle.

3. Security: Safeguarding Data Assets

Data is the core moat of AI companies. We provide full-link encryption and control to prevent leaks of core datasets and model parameters.

End-to-End Encryption: Data is encrypted before leaving the collection point and decrypted only upon reaching the training server. Transit nodes cannot peek at the content.
Access Auditing: (Enterprise Feature) Detailed logs of IPs, timestamps, and download volumes for every dataset access, ensuring traceability and meeting compliance audits.

4. Experience: Designed for Data Scientists

Simplify MLOps, letting data engineers focus on algorithms rather than file transfer:

Directory Structure Support: Directly transfer folder structures containing millions of annotation files and small images without time-consuming zipping and unzipping.
Automated Integration: Trigger transfer tasks via API to achieve a closed loop from data cleaning and transfer to model training.