Rack-Scale AI Design: The End of Component Scaling
We have hit the physical limits of what a single chip can do. The new unit of compute for AI infrastructure isn't the GPU; it's the fully integrated rack.
Tag: hardware
Vision Transformer (ViT) Latency
Why Patch Size fundamentally dictates your cloud throughput entirely independently of actual parameter count when deploying Vision Transformers in production.
Switching Technologies in AI Accelerators
This post contrasts the switching technologies of NVIDIA and Google's TPUs. Understanding their different approaches is key to matching modern AI workloads, which demand heavy data movement, to the optimal hardware.
Generality vs. Specialization - The Real Difference Between GPUs and TPUs
It's not just about specs. This post breaks down the core trade-off between the GPU's versatile power and the TPU's hyper-efficient, specialized design for AI workloads.