What Tensor Cores Compute
Fused matrix multiply-accumulate units.
What Tensor Cores Compute is a free CUDA 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 CUDA Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Meet the Tensor Core
A Tensor Core is a special hardware unit on modern NVIDIA GPUs built to do one job blazingly fast: small matrix math. 🚀
One Operation: MMA
Tensor Cores compute a matrix multiply-accumulate, written D = A times B plus C. They do the whole multiply-and-add in one shot.
Whole Tiles at Once
Instead of one number, a Tensor Core handles a small matrix tile each cycle. That is why it crushes the work a plain core does element by element.
Fused Multiply-Add
The multiply and the add happen fused, so the intermediate product is never rounded on its own. That keeps more accuracy than two separate steps.
Why Matrices Matter
Deep learning and graphics are full of matrix multiplies. Tensor Cores exist because that one operation dominates so many real workloads.
The Accumulate Part
The plus C in D = A times B plus C lets you keep a running total. This accumulator is how big results are built from many small tiles.
Speed Over Plain Cores
For matrix math a Tensor Core can deliver many times the throughput of ordinary CUDA cores, because it packs a full tile multiply into one instruction.
Mixed Inputs, Wider Sums
Tensor Cores often take low-precision inputs but keep a higher-precision accumulator. You get speed on the multiply and safety on the running sum.
Born in the Volta Era
Tensor Cores arrived with the Volta architecture and grew with Turing, Ampere, and Hopper. Each generation widened the tiles and added formats.
Not for Every Kernel
Tensor Cores only help when your work looks like a matrix multiply. A simple vector add or scalar loop will not touch them at all.
A Tiny Glimpse
You reach Tensor Cores through libraries or the WMMA API. This call shape is the multiply-accumulate you will write later.
wmma::mma_sync(acc, a_frag, b_frag, acc);Quick Check
What single operation are Tensor Cores designed to accelerate?
Recap
You learned that a Tensor Core fuses a full tile-sized matrix multiply-accumulate into one fast step, perfect for the matrix math behind deep learning. Next: precision formats. 🎉
Frequently asked questions
Is the “What Tensor Cores Compute” lesson free?
Yes — the full text of “What Tensor Cores Compute” is free to read here on the web, and the CUDA 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 CUDA Academy course, upgrade to CoddyKit PRO.
What will I learn in “What Tensor Cores Compute”?
Fused matrix multiply-accumulate units. You practise CUDA 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 CUDA Academy?
No prior experience is required. CUDA 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 “What Tensor Cores Compute” 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 CUDA Academy lesson?
Yes. Every CUDA 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.
All lessons in this course
- What Tensor Cores Compute
- Mixed Precision: FP16, BF16, TF32
- The WMMA Fragment API
- Numerical Stability Tradeoffs