While everyone’s hyping the AI revolution, here’s the uncomfortable reality—training a standard 70B parameter model requires 1.4TB of GPU memory. That means buying 30+ high-end GPUs just to pool enough memory together, costing $500K+ before you even factor in power, cooling, and specialists.

Meanwhile, AI models are growing 400x every two years while GPU memory crawls at 2x. We’re creating an “AI oligopoly” where only tech giants can afford to play.

But what if there was a different path?

I just published an analysis of Phison’s aiDAPTIV+ solution, which claims to slash AI training costs by 90% through intelligent flash storage integration. The same 70B model that needs 32 GPUs? They say you can train it on just 4.

Of course, there are trade-offs (speed vs. cost), and I dig into those too.

Whether this specific solution succeeds or not, the underlying problem isn’t going away. We need alternatives to the current “AI for the elite” trajectory.

Read my full analysis: https://www.linkedin.com/pulse/ai-memory-crisis-how-one-company-thinks-theyve-solved-jack-poller-6b5ue
What do you think? Is cost the biggest barrier to AI adoption in your organization?