(1) For Google's AI chips, see
Tensor Processing Unit and
Tensor chip.
(2) For AMD's AI chips, see
AMD Instinct.
(3) For Amazon's AI chips, see
Amazon AI chips.
(4) For NVIDIA's first AI chips, see
GPU and
CUDA.
(5) For Groq's AI language processing unit (LPU), see
Groq.
(6) For the AI image generation chip, see
Normal Computing.
(7) For the photonic AI chip, see
Native Processing Unit.
(8) For wafer-scale AI chips, see
Cerebras AI computer.
(9) For low-power AI chips, see
Normal Computing and
Native Processing Unit.
(10) The primary AI chip is the graphics processing unit (GPU). As desktop computers became more powerful in the late 1990s, the GPU debuted to render graphics onto the screen for animation and video, especially for gamers. Because the GPU performs parallel operations, it became the logical processor for AI training and execution (inference), both of which require massive amounts of mathematical calculations in parallel. See
AI training vs. inference and
GPU.
Capitalizing on its vast GPU experience, NVIDIA enhanced its GPUs to become the world leader in AI chips, each of which can cost tens of thousands of dollars (see
A100,
H100,
Blackwell).
NVIDIA Grace Blackwell Superchip
AI "factories" may have hundreds of liquid-cooled server racks, each holding 36 of these Superchips that cost tens of thousands of dollars. Everything is connected via NVIDIA's NVLink. (Image courtesy of NVIDIA Corporation.)
The Versal System-on-Chip
Today's chips often include AI processing. This Versal system-on-chip (SoC) contains more than 30 billion transistors and provides circuits for AI processing (green area). It also contains programmable hardware, which means the actual circuits are programmed at startup, a rarity on any SoC (red area). See
SoC,
FPGA and
Versal.
(Image courtesy of AMD.)