TuringData is not a cloud storage provider. we are a high-performance AI storage company that specializes in delivering data to GPUs at the speed required for large-scale AI training and inference.
As enterprises and AI service providers race to deploy large-scale AI applications, the industry is entering a new phase where the key metric is no longer the number of GPUs deployed, but the volume of tokens generated efficiently and profitably.
At ATxSG 2026, TuringData's VP of International Sales & Market Expansion Nikhil Madan explains why the data infrastructure layer is the hidden bottleneck in enterprise AI — and how TuringData is solving it.
Jensen Huang put it plainly at GTC Taipei: tokens are now profitable units of revenue. The real question is how you maximize them — and the answer isn't more GPUs.
TuringData VP Nikhil Madan explored the shift from data centers to Token Factories, highlighting how AI-native data infrastructure is becoming essential for scalable, low-cost, high-performance AI.
An AI Factory is purpose-built infrastructure for producing AI at scale. Unlike traditional data centers, performance and data movement hinges on one thing. That makes storage the defining bottleneck.
A humanoid robot outran the human marathon record in Beijing, showing the rise of embodied AI. TuringData powers the data infrastructure behind its continuous learning, connecting perception, training, and real-world action.
A fundamental shift is underway in AI infrastructure: as inference becomes the dominant workload, data movement, cache efficiency, and storage architecture are becoming the key determinants of AI system performance and scalability.
TuringData Cache Fabric enables long‑context AI Agents like OpenClaw by offloading KV cache beyond GPU memory, cutting latency and token costs, and significantly boosting inference speed and scalability.
TuringData’s full-stack AI storage solution, designed to keep data in motion across the entire lifecycle—from training to inference—unlocking higher throughput, lower latency, and better economics for modern Agentic AI systems.