The domain at the most critical junction in AI — where large language models meet the silicon designed to run them. Data centre GPUs. Embedded LLM chips in devices. Edge AI in robots. Neuromorphic processors for agentic systems. Every chip that makes machine intelligence physically possible.
"LLM" — Large Language Model — is the most commercially significant three-letter acronym in the history of AI. ChatGPT is an LLM. Claude is an LLM. Gemini is an LLM. Every AI assistant, every enterprise AI system, every autonomous agent operating on language understanding is an LLM. The term is universally understood across every audience in the AI ecosystem — from researchers to investors to product teams to regulators.
"Chips" names the physical substrate. Not "silicon" (too raw), not "semiconductors" (too formal), not "processors" (too generic) — "Chips" is the commercially precise, globally understood term for the integrated circuits that run AI. Together, LLMChips.com names the most critical technology intersection of the AI era: the silicon architecture specifically designed to run large language models efficiently, whether in a petawatt data centre or a 1-milliwatt embedded device.
Full Domain Analysis →NVIDIA H100, H200, Blackwell B200 — the GPU clusters training and serving the world's frontier LLMs. The $300B+ data centre AI chip market requiring CoWoS packaging, HBM3 memory, and NVLink interconnect to run models at the scale intelligence demands.
Google TPU, AWS Trainium, Microsoft Maia, Groq LPU — the custom inference accelerator chips purpose-built to run LLMs faster and more efficiently than general-purpose GPUs. The ASIC-native LLM inference market displacing GPU incumbency at scale.
Apple A18 Neural Engine, Qualcomm Snapdragon X Elite NPU, Samsung Exynos — the system-on-chip NPU blocks enabling on-device LLM inference at 1–10W, bringing language intelligence to every smartphone, tablet, and laptop without cloud dependency.
NVIDIA Jetson, Hailo-8, Arm Cortex-M85 with Helium — the embedded AI chips running compressed LLMs at the edge: industrial sensors, smart cameras, medical devices, agricultural monitors, and IoT infrastructure operating AI inference offline at milliwatt power budgets.
Tesla FSD chip, NVIDIA Drive AGX Orin, Qualcomm Robotics RB6 — the specialised AI silicon powering autonomous vehicles, industrial robots, and humanoid robots with real-time LLM-powered perception, planning, and physical interaction capabilities.
The persistent compute substrate for agentic AI systems — inference accelerators running LLM-based planning loops continuously, memory-augmented NPUs maintaining agent context, and specialised silicon architectures for multi-agent coordination at low latency and high throughput.
From NVIDIA Blackwell to Apple A18 to Hailo-8 embedded — the complete silicon architecture stack that makes large language models computationally possible from data centre to edge device.
Read Analysis →The silicon layer of machine intelligence. Available for acquisition.