Analysis of LLM chip architecture across the complete deployment stack: data centre training GPUs, inference ASICs, mobile NPUs, embedded LLM silicon, edge AI, robotics processors, agentic AI compute, humanoid robot chips, and the tokenized payment infrastructure for AI-silicon-managed assets.
NVIDIA Blackwell to Apple A18 to Hailo-8 — the complete silicon architecture stack that makes LLMs computationally possible.
Quantised LLMs on MCUs, NPUs, and edge AI SoCs — the silicon revolution bringing language intelligence offline.
The investment case for the domain naming the most critical bottleneck in the $620B AI silicon market.
Tesla FSD, NVIDIA Drive, Qualcomm Robotics RB6 — the chips making humanoid robots LLM-native at the edge.
Memory-augmented NPUs, neuromorphic inference engines, and the specialised silicon architectures for continuous agentic AI operation.
LLM chips as the inference substrate for tokenized asset management — from embedded trading agents to robot-managed infrastructure.