How can enterprises maximize token throughput while minimizing infrastructure waste? Here is the answer.
As AI adoption accelerates across enterprises, the conversation is shifting from simply deploying GPUs to maximizing the value of every token generated.
In a recent interview with CRN Asia, TuringData Founder and CEO Jenvik Li and Vice President Nikhil Madan discussed how organizations can rethink AI infrastructure through AI-native storage, software-defined architecture, and intelligent KV Cache orchestration.
The interview explores several key industry trends:
AI Infrastructure Is Entering the Inference Era
Inference has become the dominant AI workload. Modern AI applications require faster responses, larger context windows, higher concurrency, and lower operating costs.
Better AI Doesn't Always Require More GPUs
Instead of scaling infrastructure by continuously adding hardware, organizations should maximize GPU efficiency through smarter storage architecture and memory orchestration.
AI Token Economics Will Define Competitive Advantage
As AI adoption grows, reducing the cost of generating every token becomes increasingly important. Infrastructure efficiency—not just compute power—will determine long-term AI ROI.
Software-Defined Infrastructure Enables Lean AI Deployment
TuringData's AI-native architecture enables organizations to build scalable AI infrastructure without heavy storage deployments, allowing enterprises to start small while scaling efficiently.
Read the full interview on CRN Asia: https://www.crnasia.com/news/2026/artificial-intelligence/turingdata-bets-on-smarter-storage-to-improve-ai-token-econo