Why Most AI Chips Fall Short (And What to Use Instead)

Many AI chips struggle with modern workloads. I discovered this while helping startups scale their AI infrastructure.

The problem isn't just raw performance anymore. Many popular chips fall short on memory bandwidth or power efficiency. Through my testing, I've found that a hybrid approach gives better results. I now use a three-tier setup: H100s for training, A100s for development, and custom accelerators for specific inference tasks.

The real game-changer? Looking for chips that balance compute power with memory architecture - maintaining high throughput while managing power consumption effectively. These are features many manufacturers overlook.

December 12, 2024