AI Chip Providers

Google's custom-built AI accelerator ASICs (Tensor Processing Units), spanning cloud-scale training to edge inference.

Arm's AI-optimized CPU, GPU, and NPU IP powers the vast majority of the world's mobile and edge AI silicon.

Apple's A-series and M-series chips ship a dedicated Neural Engine for on-device machine learning across iPhone, iPad, and Mac.

Intel's AI portfolio spans Xeon CPUs, Gaudi accelerators, and NPUs to help enterprises build and deploy AI at scale.

AMD's Instinct GPUs, Ryzen AI NPUs, and (following the Xilinx acquisition) adaptive FPGA compute for AI workloads.

Graphcore's Intelligence Processing Units (IPUs) are purpose-built processors designed specifically for machine intelligence.

MediaTek builds AI-integrated system-on-chip products powering smartphones, smart homes, and connected devices.

NVIDIA's GPUs remain the dominant compute platform for AI training and inference, from the datacenter to the edge.

Mythic builds analog compute-in-memory processors (AMPs) that deliver efficient AI inference at the edge.

Qualcomm's Hexagon NPUs and AI research push efficient, on-device intelligence across billions of Snapdragon-powered devices.

Blaize designs low-power AI processing chips purpose-built for edge inference in vision and video applications.

TSMC is the leading semiconductor foundry manufacturing AI chips for NVIDIA, Apple, AMD and others, using AI-driven smart manufacturing itself.

Samsung's Exynos system-on-chip line integrates dedicated NPUs for on-device AI in mobile and consumer electronics.

HiSilicon (Huawei's chip design arm) develops the Kirin and Ascend series of AI-capable processors.

Texas Instruments supplies embedded processors and microcontrollers with AI acceleration for industrial and edge devices.

VIA Technologies builds AI-enabled SoC solutions for automotive, edge, industrial, and building automation applications.

Imagination's PowerVR Vision & AI cores deliver neural network acceleration for mobile, automotive, and smart-camera markets.

Huawei's Ascend series (including the Ascend 910) are high-performance AI processors for training and inference at scale.

Microsoft Research's FPGA-based deep learning platform for real-time AI inference in the cloud and at the edge.

Groq's LPU (Language Processing Unit) is custom silicon purpose-built for fast, low-cost AI inference.

Kalray's MPPA massively parallel processor arrays capture and analyze high-throughput data flows in real time.

AWS's custom-designed Inferentia accelerators deliver high-performance, low-cost machine learning inference on Amazon EC2.