NVIDIA GTC 2025: DDN Inferno Ignites Real-Time AI with 10x Faster Inference Latency

At NVIDIA GTC 2025, DDN (DataDirect Networks Inc.) is redefining enterprise AI performance with the launch of DDN Inferno, a game-changing inference acceleration appliance that delivers sub-millisecond response times and 10x lower compute costs for real-time AI applications.

Inferno is purpose-built to eliminate inference bottlenecks, optimize GPU utilization to 99%, and integrate multimodal AI data pipelines.

By combining DDN Infinia with NVIDIA Spectrum-X’s AI-optimized networking, enterprises can accelerate AI workflows, maximize GPU utilization, and scale their AI infrastructure seamlessly.

Unlocking Next-Gen of Real-Time AI
DDN Inferno is designed to tackle two of the biggest challenges in AI inference: latency and cost. By integrating the firm’s high-performance architecture with AI-optimized networking from the largest semiconductor company, Inferno accelerates inference processing in ways never seen before:

• 10x Lower Latency – Response times now drop to sub-millisecond, ensuring instant AI decision-making in mission-critical environments.
• 12x Cost-Efficiency Gains – Early testing shows Inferno outperforms AWS S3-based inference stacks by 12x, dramatically reducing compute overhead.
• 99% GPU Utilization – Inferno removes data bottlenecks, ensuring AI workloads fully leverage GPUs for peak performance.

Powering Enterprise AI Without Limits
With Inferno, enterprises can seamlessly integrate multimodal AI workloads – from language models and computer vision to sensor fusion and real-time analytics. Whether deployed on-premises, in the cloud, or in hybrid environments, Inferno simplifies AI data integration and accelerates inference pipelines.

“Real-time AI isn’t just about speed—it’s about removing every barrier between data and intelligence,” said Omar Orqueda, SVP, Infinia engineering, DDN. “Inferno delivers the industry’s most advanced inference acceleration, making instant AI a reality while slashing costs at enterprise scale.”