6 AI Chips Explained | CPU vs GPU vs TPU vs NPU

6 AI Chips Explained | CPU vs GPU vs TPU vs NPU AI agents don't run on one chip; they never did. OpenAI's Jalapeño just made that impossible to ignore… Here are 6 processors powering the next era of AI agents. 📌 GPU (Graphics Processing Unit) → What: Parallel compute for AI training and inference → How: Thousands of cores handle matrix ops in parallel → Who benefits: AI labs, cloud providers, enterprises → Examples: NVIDIA Blackwell Ultra, AMD MI300X 📌 CPU (Central Processing Unit) → What: Orchestration and control flow for AI agents → How: Manages scheduling, memory, task coordination → Who benefits: Data centers scaling agentic AI workloads → Examples: Intel Xeon 6+, AMD EPYC 📌 TPU (Tensor Processing Unit) → What: Custom silicon for tensor operations at scale → How: Systolic arrays optimized for matrix math → Who benefits: Teams training and serving large models → Examples: Google Ironwood 7th Gen, TPU v6e 📌 NPU (Neural Processing Unit) → What: On-device AI inference at ultra-low power → How: Dedicated engines run quantized models locally → Who benefits: Enterprises needing private, edge AI → Examples: Apple M5 Neural Engine, Qualcomm Hexagon 📌 DPU (Data Processing Unit) → What: Handles networking, security, data movement → How: Offloads infrastructure tasks from CPUs → Who benefits: AI data centers, multi-agent clusters → Examples: NVIDIA BlueField, AMD Pensando 📌 ASIC OpenAI Jalapeño (New · June 2026) → What: Purpose-built silicon for LLM inference → How: Blank-slate design, no general-purpose overhead → Who benefits: AI companies serving at gigawatt scale → Examples: OpenAI Jalapeño, AWS Trainium3 📌 So why are all hardware companies pursuing different strategies? NVIDIA → GPU acceleration Intel → CPU as orchestration layer Google → TPUs for inference at scale Apple → AI agents at the edge OpenAI → custom silicon for LLM inference The future AI stack combines all of them.