1. Why Are AI Computing Costs Going Up Despite Falling AI Technology Prices?
Organizations today face unprecedented constraints that are fundamentally reshaping AI deployment strategies. Every company must deal with the reality of certain hard budgets they can’t ignore: Rack space is at a premium, GPUs are scarce, and perhaps most critically, power and electricity limitations that stifle AI performance are emerging worldwide.
This shift demands a new way of thinking about the infrastructure that powers AI innovation and services. The traditional metrics of tokens-per-second and tokens-per-GPU are becoming obsolete. The new metric that matters most is now tokens-per-watt, which reflects how much AI processing power you can squeeze out of each precious unit of electricity.
2. What Makes Storage Performance More Critical Than Storage Capacity for AI Infrastructure?
At WEKA, we’ve recognized that designing infrastructure with high performance density is no longer measured by storage capacity alone, but by how well that storage performs in the real world of accelerated AI. As the industry moves from training AI models to the age of AI reasoning and inferencing, every microsecond counts, so your storage layer can’t be a bottleneck.
Deploying high-performance storage that keeps pace with AI workloads means you can run leaner, more efficient infrastructure without sacrificing capability, and you’re using every watt of power you can acquire to its fullest extent.
A key way to do this is by extending memory capacity through KV cache optimization with WEKA’s Augmented Memory Grid. This fundamentally transforms AI infrastructure efficiency by intelligently managing how data flows between storage and compute to get the most out of hardware investments with the least amount of power. In fact, Generation IM recently noted that at WEKA we helped our customers avoid 358,000 tons of emissions in 2024, suggesting our customers are using less power to serve their AI needs.
3. How Does KV Cache Optimization Transform AI Infrastructure Efficiency and Memory Management?
WEKA’s advanced KV cache technology, Augmented Memory Grid, extends effective memory capacity beyond physical RAM limitations into token warehouses, creating a seamless bridge between high-speed storage and compute resources. This intelligent caching system removes current memory limits, minimizes data movement overhead, and dramatically improves tokens-per-watt efficiency across AI workloads.
By revolutionizing how to store and retrieve frequently accessed data, KV cache optimization enables organizations to run larger AI models on existing hardware configurations while consuming significantly less power.
4. Why Will 80% of AI Spending Focus on Inference Rather Than Training Infrastructure?
Training dominated AI’s first wave, but inference can flip the script on AI economics through efficiency improvements that deliver the most impact.
Industry experts the world over are predicting inference spending to overtake prior training budgets by large margins. This will fundamentally reshape how we think about AI infrastructure investments.
Unlike training workloads that run in controlled environments with predictable patterns, inference demands must handle real-world variability in data access while maintaining consistent performance. This makes storage design even more critical, as unpredictable access patterns in a traditional storage environment can create speed bumps that slow down the entire inference pipeline.
5. What Does the Future of Efficient AI Design Look Like?
The future of AI infrastructure isn’t about having the most GPUs or the largest storage arrays. It’s about architecting systems that deliver maximum tokens per watt. This requires rethinking every component of the stack, from storage performance to memory management to power distribution.
Organizations that master efficient infrastructure design today will have a decisive competitive advantage in the future as AI workloads continue to scale exponentially. They’ll be able to deploy more performant AI systems within the same energy footprint, achieve better ROI on their hardware investments, and scale their AI operations without bumping against the guardrails of power and space constraints.
The question isn’t whether your organization will need to prioritize efficiency. It’s whether you’ll get ahead of this trend or be forced to react when these constraints become obstacles to innovation and profitability.