TensorTonic or LeetGPU: where should you learn CUDA?
TensorTonic vs LeetGPU for learning CUDA: GPU access, multi-GPU benchmarking, problem paths, languages and pricing compared, with advice for each goal.
Is TensorTonic or LeetGPU better for learning CUDA?
TensorTonic is the better choice for learning CUDA if you want an ordered path from your first kernel to attention, the same kernel benchmarked on six real GPUs, and the ML maths behind each kernel on one account. All 35 CUDA problems are on the free plan. LeetGPU is a GPU programming challenge site.
You can open the CUDA Kernels study plan and run your first kernel on a cloud GPU from the browser, with nothing to install.
How do TensorTonic and LeetGPU compare?
The table compares the two platforms on the points that matter most when you are learning GPU programming. TensorTonic details come from our pricing page and study plans, and LeetGPU details come from LeetGPU's own website, checked on 23 October 2026.
| Feature | TensorTonic | LeetGPU |
|---|---|---|
| Kernel problems | 35 CUDA and 30 Triton problems, ordered as study plans | 50+ GPU programming challenges |
| What else you practise | ML coding, research papers, LLM internals and guided projects on the same account | GPU programming |
| GPUs | Choose from an NVIDIA T4, RTX 4090, RTX 5090, RTX PRO 6000, H100 or H200 | Real GPUs in the browser |
| Benchmarking | Timings across input sizes against a reference, with the same kernel compared side by side on different GPUs | Execution timing |
| Free plan | All 35 CUDA problems on real GPUs, plus 500+ ML coding problems | Available |
| Paid plans | Pro $12 a month or $100 a year; Plus $20 a month or $168 a year | Pro plan |
Why did I build TensorTonic?
I built TensorTonic because I wanted to learn GPU programming and could not get a working CUDA setup on my own laptop. Instead of buying an NVIDIA machine, I built a platform where your kernel runs in a ready sandbox on real GPUs, and where the ML algorithms those kernels speed up sit in the same catalog, so you can implement softmax in NumPy and then write it as a CUDA kernel in the next problem.
What does multi-GPU benchmarking show that a single GPU cannot?
Benchmarking the same kernel on several GPUs shows which resource your kernel is actually limited by, because GPUs differ in memory bandwidth, cache size and the number of streaming multiprocessors. A timing on one GPU tells you how fast the kernel ran there, but it cannot tell you why.
A memory-bound kernel, such as element-wise addition, speeds up roughly in line with memory bandwidth when you move to a faster GPU, because it spends most of its time waiting for data. A compute-bound kernel, such as a tiled matrix multiplication, follows the GPU's arithmetic throughput instead. If your kernel gains much less than the hardware should allow, that gap points to a problem in the kernel, such as uncoalesced memory access or low occupancy.
On TensorTonic you pick the GPU for each run, from an NVIDIA T4 to an H200, and after a passing submission you see your timings across input sizes next to a reference implementation on the same GPU. Putting results from different GPUs side by side is how you learn to write kernels that hold up on hardware you did not tune them for.
How do you learn CUDA on TensorTonic?
You learn CUDA on TensorTonic by working through the CUDA Kernels study plan in order, which starts with one thread per element and builds up to reductions, shared-memory tiling, convolutions and attention. The CUDA practice problems guide walks through all 35 problems and explains what each stage teaches.
After CUDA, the Triton Kernels study plan covers 30 problems written in Python at the block level, and solving the same operation in both languages shows what Triton handles for you. When you get stuck, Toni, the AI assistant in the editor, can see your kernel and the failing test and explain what went wrong.
Which should you choose for your goal?
The right platform depends on why you are learning GPU programming, so the advice below starts from your goal.
- You are learning CUDA from zero: choose TensorTonic, because the study plan puts the problems in an order where each one uses what the previous one taught.
- You want GPU skills for ML work: choose TensorTonic, because the kernels sit next to the ML problems they speed up, from softmax and layer norm to attention.
- You want to understand performance across hardware: choose TensorTonic, because you can run the same kernel on six GPUs and compare the results side by side.
- You do not want to pay: choose TensorTonic, because all 35 CUDA problems and 500+ ML coding problems are on the free plan.
- You already use LeetGPU: keep your progress there and use TensorTonic for multi-GPU comparisons and for the ML context around each kernel.
Practice this
These problems follow the CUDA path from a first kernel to fused attention, and each one runs on a real GPU against tests.
- Vector addition: write your first kernel with one thread per element and see its timings.
- Sum of an array: build a parallel reduction, the pattern behind every sum, mean and norm.
- Tiled matrix multiplication: use shared memory to reuse data and compare the result across GPUs.
- FlashAttention forward in Triton: compute attention in tiles so the full score matrix is never stored.