TensorTonic or Deep-ML: which ML practice platform fits you?
Deep-ML vs TensorTonic compared on what you practise, pricing, GPU kernels on real hardware, research-paper tracks and AI help, with advice for each case.
Is Deep-ML or TensorTonic better for ML practice?
TensorTonic is the better fit for most ML learners, and you can use it for free: the free plan includes 500+ ML coding problems with unlimited submissions, all 35 CUDA kernels run on real GPUs and 13 research-paper tracks. Deep-ML is another ML coding practice site, and TensorTonic is the one built around GPU kernels benchmarked on real hardware and step-by-step paper implementations.
TensorTonic is an ML coding practice platform where learners implement algorithms, CUDA kernels and research papers from scratch, with multi-GPU benchmarking and an in-editor AI assistant, Toni. You can start on the free plan and open the problem library without a card.
How do Deep-ML and TensorTonic compare?
The table compares the two platforms on the points people ask about most. TensorTonic details come from our pricing page, and Deep-ML details come from Deep-ML's own website, checked on 23 October 2026.
| Feature | TensorTonic | Deep-ML |
|---|---|---|
| What you practise | ML algorithms, CUDA and Triton kernels, research papers and guided AI projects | ML coding problems and labs |
| Free plan | 500+ problems, all 35 CUDA kernels on real GPUs and 13 paper tracks, with unlimited submissions | Available |
| Paid plans | Pro $12 a month or $100 a year; Plus $20 a month or $168 a year | Premium $11 a month, billed yearly at $132 |
| GPU kernels | CUDA and Triton kernels timed on six real GPUs against a reference | CUDA and Triton problems |
| Research papers | Papers split into step-by-step coding tracks: 13 on Free, 20 on Plus | Guided projects, such as building a GPT |
| AI help | Toni, an assistant in the editor that sees the problem you are solving | Zero, an AI study assistant |
| Interview preparation | Cracking ML plan, timed mock assessments and ML system design on Plus | Company interview tracks |
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, and I also wanted one place to practise implementing ML models from scratch with code that gets checked. Instead of buying an NVIDIA machine and moving between several sites, I built a platform where your code runs in a ready sandbox on real GPUs, and where ML algorithms, kernels and research papers share one catalog and one progress history.
That background explains where TensorTonic puts its effort: the path from the math behind an algorithm, to a NumPy or PyTorch implementation, to a GPU kernel for the same operation, without installing anything on your machine.
Where is TensorTonic stronger?
TensorTonic is stronger for GPU programming, research-paper implementation and help while you code, which are the three areas the platform was built around.
GPU kernels benchmarked on real hardware. In the CUDA Kernels and Triton Kernels study plans you write kernels in the browser and choose the GPU that runs them, from an NVIDIA T4, RTX 4090, RTX 5090, RTX PRO 6000, H100 or H200. After a passing submission you see your timings across input sizes next to a reference implementation, and you can compare the same kernel on different GPUs side by side. The CUDA practice guide walks through all 35 CUDA problems in order.
Research papers as coding tracks. The research section splits influential papers into a sequence of problems, so you rebuild an architecture such as the Transformer one component at a time and test each step before moving on. The free plan includes 13 paper tracks, and Plus raises that to 20, including modern LLM architectures such as GPT-2 and DeepSeek.
Toni, the assistant in the editor. Toni can see the problem you are working on and your code, so you can ask for a hint or an explanation of a failing test without copying anything into another tool.
How much do Deep-ML and TensorTonic cost?
TensorTonic's free plan costs $0 forever, with no card needed. Pro costs $12 a month or $100 a year and adds study plans, quizzes and timed mock assessments, while Plus costs $20 a month or $168 a year and adds modern LLM architecture tracks and ML system design. Deep-ML Premium costs $11 a month, billed yearly at $132, according to its website on 23 October 2026. The Is TensorTonic free? guide lists everything each TensorTonic plan includes, and students can apply for a discount from the pricing page.
Which should you choose for your situation?
The right platform depends on what you are trying to do over the next few months, so the advice below starts from your situation rather than from either product.
- You want to learn CUDA or Triton without owning a GPU: choose TensorTonic, because kernels run on real cloud GPUs from the browser and you see how your timings compare with a reference on each GPU.
- You want to implement research papers: choose TensorTonic, because each paper is split into tested steps that build up to the full architecture.
- You are preparing for ML engineer interviews: TensorTonic Pro gives you the Cracking ML plan and timed, scored mock assessments, and the ML engineer interview guide includes a six-week plan that uses them.
- You never plan to pay: choose TensorTonic, because its free plan already covers 500+ ML problems, all 35 CUDA kernels on real GPUs and 13 research-paper tracks with unlimited submissions, which is more kinds of practice at $0.
- You already use Deep-ML: keep your progress there and use TensorTonic's free plan for GPU kernels and paper tracks, which is where the two platforms differ most.
Can you use Deep-ML and TensorTonic together?
Yes. Many learners use more than one practice site, and the two platforms overlap mostly on classic ML algorithms. A practical split is to keep your existing routine for general drills and use TensorTonic for the work that needs real GPUs, step-by-step paper implementations and interview mock assessments.
Practice this
These problems show how TensorTonic handles ML code, GPU kernels and research papers, and each one runs against tests in the browser.
- Implement numerically stable softmax: shift the logits before exponentiating so large values do not overflow.
- Adam optimizer step: write one Adam update in NumPy with both running moments and bias correction.
- Vector addition in CUDA: write your first kernel and see its timings on a real GPU.
- Scaled dot-product attention: build the attention step of the Transformer paper track.