TuringData – Powering the Age of Reasoning

We are TuringData, named after Alan Turing—the pioneer who first imagined intelligent machines and changed the course of technology forever.
We carry his spirit: curiosity, courage, and the pursuit of deeper intelligence. Every AI breakthrough begins with data— how it moves, how it scales, how it empowers ideas to become reality.
Our symbol — two interlocking blocks—represents the core of AI: Compute × Data. Modular. Scalable. Precise.
We honor where AI began, but we build for what comes next:A world where data is limitlessWhere infrastructure accelerates, not restrictsWhere intelligence flows freely—from core to cloud
We design AI-native data systems that turn complexity into clarity and capability into confidence.
When data moves without friction, innovation moves without limits.

Our Core Beliefs in AI Storage

At TuringData, we are driven by a commitment to build the data foundation that powers intelligent innovation. We believe AI storage must be:

Performance by Design
True speed is engineered from the ground up. We build storage that eliminates bottlenecks, maximizes GPU utilization, and delivers consistent low latency—so your models train faster, and insights arrive sooner.
Intelligent at Scale
Data should move seamlessly, from training to inference, across any environment. Our platform unifies and accelerates data with metadata-aware intelligence, enabling effortless scaling without complexity.
Future-Driven Infrastructure
AI never stops evolving—and neither should your infrastructure. Our elastic, cloud-native architecture adapts seamlessly to tomorrow’s models and workloads, turning storage into a strategic advantage.

AI Training Acceleration
Maximize GPU utilization and reduce bottlenecks
Train large models faster and more efficiently
Large-Scale Model Inference Optimization
Deliver low-latency, high-throughput inference
Power real-time AI services at global scale
Seamlessly move data across clouds and regions
Deliver low-latency, high-throughput inference
Ensure consistent performance for distributed AI workloads
Why It Matters
Speed: Faster training → faster innovation
Efficiency: Lower resource cost, higher utilization
Scalability: From a single cluster to a global AI cloud