Specialised AI Chips (TPUs, Trainium, etc.)

Several companies have designed chips specifically for AI workloads rather than adapting general-purpose processors. Google's Tensor Processing Units (TPUs) were among the first, purpose-built for the tensor operations that neural networks rely on. Google uses TPUs extensively for internal AI work and offers them through Google Cloud. Amazon has developed its own chips - Trainium for training and Inferentia for inference - available through AWS. These are typically cheaper than equivalent NVIDIA GPU instances, though they require some adaptation of code and workflows. Other notable entries include Intel's Gaudi accelerators, AMD's Instinct GPUs (which are gaining ground, particularly for inference), and a growing crop of startups like Cerebras, Groq, and SambaNova building radically different architectures - wafer-scale chips, deterministic inference engines, and dataflow processors. The diversity is healthy for the ecosystem but creates real challenges for AI developers. Code optimised for one chip may not run efficiently on another, and the tooling ecosystem varies widely in maturity. For most organisations, the practical question isn't which chip is theoretically best - it's which one is available, supported by the frameworks you use, and cost-effective for your specific workload. The market is evolving quickly, and the competitive landscape in 2027 may look quite different from today.