Why GPUs Need Batching
Keep the GPU busy by grouping requests.
Why GPUs Need Batching is a free MLOps Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
A GPU Is a Wide Machine
A GPU has thousands of cores built to do the same math on many items at once. Feed it one input and almost all of those cores sit idle. 🐢
One Request Wastes It
Serving a single prediction per call barely touches the hardware. The GPU spends more time waiting than computing, so your expensive card is mostly idle.
Batching Fills the Cores
A batch stacks many inputs into one tensor and runs them in a single pass. The GPU does roughly the same work, but for many requests at once.
Throughput vs Latency
Two numbers matter here. Throughput is how many predictions per second you serve; latency is how long one request waits for its answer.
Batching Lifts Throughput
Grouping requests raises throughput dramatically because fixed per-call overhead is shared. You get far more predictions from the same GPU.
The Cost of Waiting
There is a catch. To form a batch the server must wait briefly for more requests to arrive, which adds a little latency to each one.
Static Batching
The simplest form is static batching, where the client itself sends a fixed-size batch. It works for offline jobs but not for live, one-at-a-time traffic.
Dynamic Batching
Dynamic batching lets the server group separate single requests on the fly. Triton Inference Server can do this for you with no client changes.
Triton Enters
NVIDIA Triton Inference Server hosts models and includes a scheduler that forms batches automatically to keep the GPU fully fed.
Why It Matters for Cost
A well-batched GPU serves many more users per dollar. Batching is often the cheapest way to cut your inference bill before buying more hardware. 💰
The Goal Ahead
Your job is to keep the GPU busy without making any single user wait too long. The rest of this course tunes that balance in Triton.
Quick Check
Why does running one input at a time waste a GPU?
Recap
You saw that GPUs need many inputs at once. Batching groups requests to lift throughput for a small latency cost, and Triton can batch dynamically for you. 🙌
Frequently asked questions
Is the “Why GPUs Need Batching” lesson free?
Yes — the full text of “Why GPUs Need Batching” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Why GPUs Need Batching”?
Keep the GPU busy by grouping requests. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Why GPUs Need Batching” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.