GPU · AI Chips
Graphics Processing Unit / AI Chip
What is a GPU?
- A parallel-processing chip that computes with thousands of cores at once
- The key hardware that determines AI training and inference speed
- Instead of buying, you can rent GPUs through the cloud
Once GPUs arrive, the bottleneck in an AI project moves. It used to be that half of every meeting was about which model to use; now the schedule is dominated by when a GPU will free up to run that experiment. The queue for compute is the development timeline. With the same model and the same data, if GPU allocation slips two weeks, the pilot report slips two weeks. Securing GPUs means you can run a few more experiments; not securing them means ideas go stale in the queue. That is why, in practice, the GPU conversation is not a technology choice but a decision that ties budget, schedule, and security together.
A GPU’s profile depends on how you use it. Training is a burst of dozens to hundreds of hours, so renting high-end cards for a short time makes more sense, while inference runs all day at low load, so an always-on server fits. The real criterion for choosing specs is usually VRAM (graphics memory) capacity. Loading a model with 7 billion parameters at 16-bit needs about 14GB for the weights alone, so a 24GB-class card with some headroom is the practical floor. Also, a GPU is not the same thing as an AI chip. Chips specialized for particular operations, such as NPUs and TPUs, deliver better performance per watt, but success often hinges less on performance figures than on compatibility with a software ecosystem like CUDA.
The most common miscalculation in practice is treating “GPU card price = cost of adoption.” A single data-center-grade GPU server draws several kW per rack and generates substantial heat; plug it into an existing server room and the breaker trips or the cooling cannot keep up. Power upgrades, cooling, and floor-space work often get added later, forcing teams to rewrite the original approval request. An even more common misconception is “Let’s buy it first; we’ll find a use for it.” An organization that needs training only a few days a month and buys a server first will see utilization stay in the single digits, and from then on the GPU is not a strategic asset but a depreciating burden. In the opposite direction, there are plenty of cases where sensitive data was uploaded to an external cloud without review, an internal audit flagged it, and the project went back to square one.
Jaryong KooMarketing PhD and leading authority on AI, data analytics, and personal branding
Seungtae ParkAX practitioner who builds AI business development and marketing automation tools and teaches from that hands-on experience| Category | CPU | GPU |
|---|---|---|
| Core design | A few powerful cores | Thousands of simple cores |
| Processing method | Sequential processing of complex tasks | Simple calculations, massive parallel processing |
| Role in AI | Control and general-purpose computing | Core engine for training and inference |
GPUs went mainstream in 1999 when Nvidia launched the GeForce 256 as a graphics-only processor. In 2012, the deep learning model AlexNet, trained on GPUs, won an image recognition competition by a wide margin, the turning point that made the GPU the standard hardware for AI computing.
Consider a hypothetical case. Company A, an auto parts maker with 420 employees and KRW 180 billion (≈ US$134,670,100) in annual revenue, launched an equipment anomaly detection model and an internal policy chatbot in the same quarter. The information strategy team requested approval for two GPU servers at KRW 30 million (≈ US$22,400) each, but the CFO put it on hold, asking 'How many hours a week will you actually use that?'
- When the head of the information strategy team went back over two weeks of research notes and work logs, only about 40 hours a month went to training; everything else was light inference requests.
- A production engineering manager ran the anomaly detection model on cloud GPUs and finished training in six weeks for a usage fee of KRW 1.8 million (≈ US$1,350). Buying a server would have tied up KRW 60 million (≈ US$44,900).
- The chatbot, however, had to read HR and contract documents, so the legal team objected to sending data outside the company. Based on a measured result, 'a single 24GB card can run 7B model inference', they bought just one in-house server.
- But when they went to install the equipment, the server room’s rack power limit was 3kW and the circuit breaker could not handle it. The facilities team spent another KRW 11 million (≈ US$8,230) on a power upgrade and cooling reinforcement, items that were not in the original approval request.
- Six months later, after splitting the work (training in the cloud, inference on its own server), projected annual costs fell from KRW 98 million (≈ US$73,300) to KRW 34 million (≈ US$25,400), and chatbot response time dropped from an average of 6 seconds to 1.8 seconds.