DDN Eliminates GPU Waste Spiral with Industry-Leading KV Cache Performance for AI Reasoning

DDN, a company in AI and data intelligence solutions, unveiled new performance benchmarks demonstrating how its AI-optimized the firm’s Infinia platform eliminates GPU waste and delivers the fastest Time to First Token (TTFT) in the industry for advanced AI reasoning workloads.

As AI models evolve from simple chatbots into complex reasoning systems capable of parsing million-token contexts, organizations are facing a new economic challenge: the hidden cost of context. Each millisecond of latency compounds across millions of interactions, creating a spiral of inefficiency, lost revenue, and underutilized infrastructure.

“Every time your AI system recomputes context instead of caching it, you’re paying a GPU tax – wasting cycles that could be accelerating outcomes or serving more users,” said Sven Oehme, CTO, DDN. “With DDN Infinia, we’re turning that cost center into a performance advantage.”

“I think the most important thing we need to understand – AI is a business of intelligence and DDN helps us maximize our GPUs and store intelligence and do it in a very scalable manner,” said Vikram Sinha, president director and CEO, Indosat Ooredoo Hutchison. “It really helps us do it at a TCO level, which is very, very competitive – and that is why we choose DDN.”

The Economics of AI Inference Have Changed
AI leaders such as NVIDIA have stated that agentic AI workloads require 100x more compute than traditional models. As context windows expand from 128K tokens to over 1M, the burden on GPU infrastructure skyrockets – unless KV cache strategies are deployed effectively.

Recent DDN benchmarks highlight the delta:

• Traditional recompute approach (112K token context): 57s processing time
• DDN Infinia with KV Cache: 2.1s loading time
• Result: Over 27X faster performance

This isn’t just a performance win – it’s a fundamental shift in the economics of AI inference at scale. Traditional AI systems waste vast amounts of GPU cycles repeatedly reprocessing the same context for every prompt or user interaction. This inefficiency creates what DDN refers to as the GPU waste spiral-a compounding drag on performance, cost, and energy usage.

DDN’s key-value (KV) cache architecture breaks this cycle by intelligently storing and reusing previously computed context data. This reduces the need to reload and reprocess tokens, cutting input token costs by up to 75%. For enterprises running 1,000 concurrent AI inference pipelines, this translates to as much as $80,000 in daily GPU savings-a staggering amount when multiplied across thousands of interactions and 24/7 operations. By removing this hidden cost layer, DDN not only accelerates response times but also unlocks new levels of economic viability for scaling generative AI in real-world production environments.

Real-World Customer Impact
Without KV cache, returning customers who resume conversations hours or days later force AI systems to reprocess entire histories – burning 10+s per interaction and thousands of GPU cycles. With DDN Infinia, those cached contexts are instantly accessible, maintaining relevance and real-time responsiveness.

Why DDN Infinia Outperforms
Engineered for next-gen AI workloads, DDN Infinia offers:

• Sub-Millisecond Latency: Under 1ms vs. 300-500ms in traditional cloud storage
• Massive Concurrency: Consistent 100K+ AI calls per second
• NVIDIA Integration: Purpose-built for H100s,GB 200s, DPUs and more
• IO500-Proven Leadership: Consistently ranked among the highest-performing data platforms globally

Future-Proofing AI Reasoning at Scale
With the rapid rise of Retrieval-Augmented gen (RAG), LLM agents, and multi-modal AI systems, inference is now a real-time, high-throughput operation. DDN’s elastic, GPU-optimized platform ensures AI infrastructure can scale with context growth – not be crippled by it.

“This is a strategic inflection point,” said Alex Bouzari, CEO and co-founder, DDN. “In AI, speed isn’t just about performance – it’s about economics. DDN enables organizations to operate faster, smarter, and more cost-effectively at every step of the AI pipeline.”