Specialized processors (GPUs, TPUs, and purpose-built AI chips) designed to run the matrix calculations that machine learning depends on, far faster than general-purpose CPUs. Hardware determines how quickly models train, how cheaply inference runs, and what AI is economically viable to deploy.
Also known as AI chips, GPU acceleration, AI accelerators
Running a neural network at scale means performing billions of matrix multiplications. Standard CPUs are designed for sequential computation and handle these tasks slowly. Specialized AI accelerators are designed for massive parallelism, running thousands of operations simultaneously, which is exactly what neural network math requires.
The most widely recognized category is the GPU (graphics processing unit), originally designed for rendering video game visuals but repurposed for AI because of its parallel architecture. More recently, custom chips like Google’s TPUs (tensor processing units) and purpose-built inference chips from various manufacturers have expanded the hardware landscape significantly.
For most agencies, the hardware layer is invisible: it is handled by cloud providers who offer API access to models running on proprietary infrastructure. Understanding it still matters for cost modeling, latency expectations, and evaluating what AI capabilities are realistically on-premise versus cloud-only.
Agencies don’t buy GPU clusters, but the hardware layer affects everything about what AI can do and what it costs. Understanding it helps agencies make better decisions about tool selection, vendor evaluation, and infrastructure advice to clients.
Cost is a hardware question. When an agency runs large volumes of AI inference (content generation, translation, classification at scale), the cost per operation is a function of the hardware running it. Cloud APIs abstract this but do not eliminate it. Volume decisions should account for what those API calls are actually paying for.
Latency is a hardware question. Real-time AI applications (live chatbots, instant creative generation in a client-facing tool) require inference speeds that only specific hardware can deliver. If a tool promises sub-second generation, it is running on hardware optimized for that performance profile.
On-premise versus cloud is partly a hardware decision. Some clients with sensitive data requirements want AI running locally rather than on third-party cloud infrastructure. Whether that is viable depends on what hardware is available on-premise and what model sizes it can support at acceptable speed.
An agency evaluating AI video generation tools for a high-volume campaign notices significant performance differences between two platforms. One produces a 15-second output in under a minute; the other takes eight minutes for the same render. The difference is infrastructure: one platform runs on optimized inference hardware, the other on shared general-purpose compute. For a campaign requiring 500 variations, that difference moves from an inconvenience to a project scheduling issue. The hardware spec sheet does not need to be read in detail. The output times tell the story.
The generative AI foundations module of the workshop covers how today’s models work, what they can and can’t do, and how to choose between them.