Alternatives
Products that do what Run PyTorch locally with a remote GPU backend does
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…
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Instantly create GPU instances over SSH. Instances boot a custom image with PyTorch, Jupyter, and the CUDA toolkit installed by default.
Jan 2026 · gpu.st
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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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2015 · github.com
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2017 · github.com
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Quick note on how it works and how I've done my batch embedding engine IgniteMS. The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper. I built it this way because when you use more than couple of GPUs, the GPUs stop being the problem. CPU cannot feed them fast enough. One A100 can go through batches faster than Python can tokenize and feed, so the GPU just sits there idle waiting for work. Most of my time went into optimizing this. At 8 GPUs that was basically the entire…
Jun 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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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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2016 · github.com
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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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2019 · github.com
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Our company Vertex.AI has been working on this for a while but this is the first public release. We're starting with using PlaidML to bring OpenCL support to Keras and more frameworks, platforms, etc are coming. Yes, this means you can use use your AMD GPU for deep learning dev. Sorry, no Mac or Windows support yet although the brave can try building from source (it should work). http://vertex.ai/blog/announcing-plaidml https://github.com/plaidml/plaidml
2017
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2019 · github.com
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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
2025
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I've been building a C++ tensor library that tries to bring the ergonomics of NumPy/PyTorch to native code. The main goals were: - Familiar API (operator overloading, method chaining, same function names) - Actual GPU acceleration via Metal (not just matmul, but all ops) - Cross-platform with SIMD on x86/ARM/RISC-V via xsimd - einops-style rearrange/reduce patterns It started because I wanted NumPy's API but needed to deploy on edge devices without Python. Ended up going deeper than expected (28k LOC+) into BLAS backends, memory views, and GPU kernels. Some things I'm…
Feb 2026
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