The Cost of Training Is Exploding
Training a frontier large-language model now costs upwards of $100 million in raw compute. Even fine-tuning a 70B-parameter model can run into six figures when you factor in reserved instance pricing, egress fees, and the engineering time lost to capacity waitlists. For startups and research labs operating outside the Fortune 500, these numbers are existential.
Demand Is Outpacing Supply
NVIDIA shipped roughly 550,000 H100 GPUs in 2025, and virtually every one of them was spoken for before it left the fab. Cloud providers impose multi-month lead times, and spot-instance pricing swings wildly — sometimes 3× within a single week. The result is a two-tier market: companies with billion-dollar cloud contracts get predictable access, while everyone else scrambles for scraps.
Why Centralization Makes It Worse
The Big Three clouds capture over 65% of all GPU compute spend. That concentration gives them pricing power, lock-in leverage, and zero incentive to lower margins. Researchers end up paying a 40–60% markup over bare-metal costs, and they accept it because there is no viable alternative — until now.
Decentralization Is the Answer
A decentralized compute network aggregates idle GPUs from data centers, mining farms, and enterprise clusters worldwide. By matching supply directly with demand — no middleman markup, no reserved-instance games — the cost per GPU-hour drops dramatically. Providers earn more than they would reselling to a single tenant, and consumers pay less than any hyperscaler can offer. That's the model VexNode is building, and it's why decentralized compute isn't a nice-to-have — it's an inevitability.