AI Hardware & Chips

Wafer-scale computing (Cerebras) builds entire AI processors on single silicon wafers. Dataflow architectures (SambaNova, Graphcore) optimize data movement for AI workloads. Deterministic inference engines (Groq) deliver consistent, predictable latency. RISC-V-based AI cores (Tenstorrent) offer customizable, royalty-free processor design. Photonic computing uses light for energy-efficient matrix multiplication. Neuromorphic chips mimic brain architecture for ultra-low-power AI at the edge.

Custom inference chips serve AI applications where cost-per-query determines economics (chatbots, search, recommendation). Edge AI chips enable on-device inference for autonomous vehicles, smartphones, and IoT. Training accelerators compete for the $billions in annual AI model training compute. Domain-specific chips optimize for particular model architectures (transformers, diffusion models, graph neural networks).

The AI accelerator chip market exceeded $50 billion in 2025 and is projected to reach $200+ billion by 2030, driven by insatiable demand for AI training and inference compute. NVIDIA captures 80%+ of data center GPU revenue, but the inference market (which will exceed training in total compute demand) creates opportunities for specialized architectures.