Definition
A GPU is a chip packed with thousands of small cores that do the same simple calculation on many numbers at once — exactly the math AI runs on.
At a glance
- Built for video-game graphics, but the same design turned out perfect for AI math.
- A CPU handles a few tasks in sequence; a GPU runs thousands of small sums side by side.
- The same large job can take days on a GPU versus months on a CPU.
- GPUs are costly and in short supply, which is why AI infrastructure is so expensive.
Why a GPU beats a CPU for AI
A CPU is a few smart workers solving problems one step at a time; a GPU is a stadium of simpler workers doing the same sum all at once[4]. AI needs the same arithmetic repeated billions of times, not clever logic, so parallel wins big[1].
What AI is actually doing
AI models — chatbots included — are giant grids of numbers being multiplied, an operation called matrix multiplication[3]. It splits into many independent pieces a GPU can crunch at once, which is why the GPU powers the AI boom[2].
What it means for your business
Few companies buy GPUs outright. Most rent computing time from cloud providers like Google, Microsoft, or Amazon, paying only for what they use.
Bottom line
A GPU turns months of AI work into days; for most businesses the real question is how much cloud GPU time your plans will need.
References
- Why GPUs Are Great for AI — NVIDIA. NVIDIA blogs.nvidia.com
- What is a GPU? An expert explains the chips powering the AI boom — The Conversation. The Conversation theconversation.com
- What is a GPU and Its Importance for AI — Google Cloud. Google Cloud cloud.google.com
- Why GPU and Not CPU for AI Parallel Processing — GigeNET. GigeNET www.gigenet.com
Comments
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