I recently watched a video by Theo Browne (t3dotgg) that explored the controversial idea of whether we’re approaching the "end of the GPU era."

The title is bold, but the discussion highlights a critical shift in where computing is heading. It’s not that GPUs are disappearing; it’s that their role is evolving as AI moves from experimentation to production.

Training vs. Inference One key takeaway is the massive difference between training models and running them.

• Training is compute-heavy and requires raw GPU power.

• Inference (running the model for users) happens at a massive scale, where efficiency and power consumption matter more than raw speed.

Because of this, we are seeing a move toward specialized hardware (like LPUs or dedicated accelerators) that complements GPUs rather than replacing them.

What this means for developers As a developer, I find this shift towards heterogeneous computing exciting. It pushes us to think more about system design and efficiency. We can't just throw raw compute at every problem anymore; we have to choose the right architecture for the specific task.

The GPU era isn’t ending. It’s just growing up.