Alternatives
Products that do what LeetGPU, a playground to learn practice and hone your GPU programming. does
Learn, write, practice CUDA programming on LeetGPU.com, an online CUDA playground for anyone to write and execute CUDA code without needing a GPU and for free
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After the incredible response to our launch of the first online CUDA playground, we have just shipped something we think all you GPU programming and ML enthusiasts will love. Introducing LeetGPU Challenges--the place to compete on writing the fastest CUDA kernels. We have problems like matrix multiplication, agent simulation, multi-head self-attention, with more dropping every couple of days! We have a lot of really cool things coming up, including support for PyTorch, TensorFlow, JAX, TinyGrad; Multi-GPU programs; H100, V100, A100 GPU options Give it a try and let us know what you think!
2025 · leetgpu.com
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Thought the resources for GPU arch were lacking, so here we are
Apr 2026 · jaso1024.com
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This is clangd extended to give feedback on host and device parts of cuda, including things like syntax errors within inline ptx. Actual cuda as it is written or the clang dialect as you prefer. I like the tarball release as that's a set of x86 binaries that run as your normal user. There's other good stuff in the package too - it's a complete cuda to amdgpu or nvptx compiler toolchain. Questions / feedback welcome here or to [email protected] or via https://discord.com/invite/KNpgGbTc38 etc
Mar 2026 · docs.scale-lang.com
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I built this mostly because I love the intersection of game AI, high-performance computing, and poker. I’d love for anyone interested in game theory or CUDA optimization to tear it apart, test the accuracy, and give me feedback. Happy to answer any questions about the algorithms, the transition from CPU to GPU, or poker AI in general!
Jul 2026 · bupticybee.github.io
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Kshitij and Rahul here. We're building Playground, an open-source sandbox that lets your non-technical teams prototype directly on production codebases, without bugging you (developers) to spin up and maintain another repo. The repo is here: https://github.com/B1u3B01t/design-playground. We've been working together for six years. Last year we built an AI design agent because we thought designers were the biggest bottleneck. But we were wrong. After talking to teams, we realised designers, PMs, founders and even sales people all wanted to quickly change and explore flows…
Jul 2026
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Complete llama.cpp tutorial for 2026. Install, compile with CUDA/Metal, run GGUF models, tune all inference flags, use the API server, speculative decoding, and benchmark your hardware. https://vucense.com/dev-corner/llama-cpp-tutorial-run-gguf-m...
Apr 2026
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It's annoying having so many ML and GS training tools rely exclusively on cuda/nvidia . So for our open-source command-line gaussian splat converter/compressor, we decided to try WebGPU instead. It's working well so far and a single codebase runs on Linux, MacOS and Windows without too much fuss. This is mostly thanks to Google's dawn project <3. Eventually some of this could also run directly in the browser.
Sep 2025 · github.com
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Hi everyone, Please checkout compute.cx which is a simple cli interface for using on demand GPUs from RunPod and HotAisle. I created this because I really like the ease of modal.com for severless gpu access, but don’t always want to pay their markup. Compute.cx gives the same DX but on public on-demand GPUs like runpod and hotaisie. Please try it out, and write to me [email protected] for any questions/suggestions, or file a bug report on https://github.com/theoriclabs/docs.compute.cx Thanks! Harsh Gupta https://x.com/hargup13 P.S. BYOK AWS, GCP and…
17d ago · compute.cx
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2019 · dev.to
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Hi all, sharing a directory of GPTs I made in a few hours after OpenAI dev day. Hope you find it useful!
2023 · topgpts.ai
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We'd like to introduce HN to Spell, which is a tool for easily running ML/DL jobs remotely. As Deep Learning has grown we see engineers and researchers struggle to incorporate running on GPUs into their workflow. So we built Spell to be the easiest way to get code running elsewhere - like the bash '&' operator but for remote machines. Sign up for an account at https://web.spell.run/waitlist, which includes $300 in credits for GPU time. There's a waitlist, but we'll be approving accounts as they come in. Here are some of the features we really wanted and built into Spell:…
2018
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Open-source tutorial series teaching real-time graphics programming with SDL's GPU API. Covers everything from Hello Window to SSAO, with math lessons, engine lessons, and a UI track building font rendering from scratch. Every lesson is a standalone C program with commented code explaining why, not just what. The whole project was built with Claude Code. Each lesson also distills into a reusable Claude Code skill — copy them into your own project and build games with AI that actually understands the GPU patterns.
Feb 2026 · github.com
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Hi HN! I've been hacking on this side project for the last month or two with the goal of making it dead simple to use cloud GPUs. I ran into this problem personally during the phd, and built my own tooling around it. I always thought it'd be fun to try to turn that tooling into a more general product... and bitbop.io is the result! All you have to do is run `ssh bitbop.io`, and you get your own personal dev GPU workstation in the cloud. Looking forward to hearing your thoughts!
2024 · twitter.com
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Feb 2026 · blog.vtemian.com
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I integrated a remote GPU execution backend into PyTorch through the same system that custom hardware accelerators get integrated into PyTorch. You can create a remote machine and obtain its CUDA device whenever you want to create or move tensors onto the remote GPU. machine = mycelya_torch.RemoteMachine("modal", "A100") cuda_device = machine.device("cuda") x = torch.randn(1000, 1000, device=cuda_device) y = torch.randn(1000, 1000).to(cuda_device) I made it reasonably performant by having most operations dispatch asynchronously whenever possible. For cases where slow performance is…
Oct 2025 · github.com
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