TPU / Custom ASIC

While GPUs are general-purpose parallel processors repurposed for AI, TPUs and custom ASICs are designed with a single mission: execute matrix multiplications and activation functions as efficiently as physics allows. Google's TPU, for example, contains a systolic array — a grid of processing elements that pipelines matrix operations without the memory-bandwidth overhead that limits GPU efficiency on certain workloads.

The enterprise custom ASIC landscape has expanded dramatically. Google offers TPU v5 via Google Cloud for both training and inference. AWS provides Trainium (training) and Inferentia2 (inference), both available as managed EC2 instances with the Neuron SDK. Groq has commercialized its Language Processing Unit (LPU), which targets deterministic, ultra-low-latency inference for transformer models. Cerebras and SambaNova offer full-system AI computers for on-premise or private cloud deployments with wafer-scale processor designs.

The trade-off is framework specificity. CUDA-based GPU workloads run on NVIDIA hardware with minimal modification. Moving a workload to TPUs or custom ASICs requires recompilation, often framework-specific optimizations, and in some cases rewriting portions of the model serving stack. The ROI calculation depends on scale: organizations processing millions of daily inference requests typically find the engineering investment worthwhile; organizations at lower volumes generally do not.