Alex Medick

Discover how TensorWave is scaling open source AI with AMD GPUs and ScalarLM and why infrastructure fixes are key to AI’s future.

Why AI Can’t Scale Without Infrastructure Fixes

Artificial intelligence is pushing the boundaries of what’s possible, but it’s also straining the very foundations it runs on. In a recent episode of the DataStax Open Source Data podcast, TensorWave CEO Darrick Horton explained why scaling AI isn’t just about smarter algorithms - it’s about solving the infrastructure puzzle.









The Energy Strain Is Real

What used to be considered massive (10 to 20 megawatts of power) now looks quaint. Today, hyperscale AI players are consuming 100 megawa

Artificial intelligence is pushing the boundaries of what’s possible, but it’s also straining the very foundations it runs on. In a recent episode of the DataStax Open Source Data podcast, TensorWave CEO Darrick Horton explained why scaling AI isn’t just about smarter algorithms - it’s about solving the infrastructure puzzle.









The Energy Strain Is Real

What used to be considered massive (10 to 20 megawatts of power) now looks quaint. Today, hyperscale AI players are consuming 100 megawatts or more, with demand pushing toward gigawatt-scale clusters. To put that in perspective: a single deployment can draw as much power as an entire city. Without new approaches to energy and efficiency, this trajectory is unsustainable.


Brute Force Isn’t a Strategy

Despite breakthroughs in model design, the dominant approach to training remains brute force: throw vast amounts of data and compute at the problem and hope for better results. Horton calls this out as reckless at scale. Efficiency, both in algorithms and in hardware utilization, has to become a first-order principle.


Infrastructure Is Strategy

AI doesn’t scale on software alone. Hardware, networking, and power are equally critical. Horton argues that solving AI’s scaling challenges means rethinking the entire stack, embracing open-source hardware, building next-generation networking, and even exploring alternative energy sources like nuclear power. Without these changes, real AI scale will remain out of reach.


The Power of Openness

Proprietary ecosystems lock innovation behind walls. Horton points to open infrastructure as the great equalizer, empowering startups and challengers to compete without being trapped in monopolistic supply chains. Openness accelerates iteration, lowers costs, and democratizes access to AI.


TensorWave’s Playbook

TensorWave is betting on this future. From building out liquid-cooled AMD GPU clusters to introducing projects like ScalarLM, the company is proving that infrastructure-first thinking isn’t optional, it’s the key to enabling the next generation of models and applications.


Scaling AI, Responsibly

The conversation doesn’t stop at compute. Scaling AI responsibly means addressing broader questions: How do we balance power demands with sustainability? Who gets access to the infrastructure shaping the future of intelligence? These aren’t side issues, they’re central to how AI will reshape industries and society.

Bottom Line

 * AI scale = infrastructure scale.
 * Efficiency beats brute force.
 * Openness accelerates innovation.
 * Your infrastructure strategy is your AI strategy.

AI’s future won’t be defined by bigger models alone. It will be defined by the infrastructure choices we make today.


About TensorWave

TensorWave is the AI cloud purpose-built for performance. Powered exclusively by Instinct™ Series GPUs, we deliver high-bandwidth, memory-optimized infrastructure that scales with your most demanding models—training or inference.

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Why AI Can’t Scale Without Infrastructure Fixes

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