Alternatives
Products that do what RunSnack — Your GPU. One link. The world does
gpu share, free, runsnack
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I'm continuing to improve my RunsOn tool for launching self-hosted runners for GitHub Action on AWS, this time with support for any GPU-enabled instance type from EC2, and using the official Deep Learning AMIs as the runner image. Much cheaper than the official GitHub Actions runners, and accessible on any GitHub plan.
2024 · runs-on.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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2019 · 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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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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I've made a GPU comparison site: https://gpu-prices.com/US/ Yes, there was one yesterday - I got beaten to it. This one is different in that I've put a lot of work into categorisation, so you can filter down quite precisely. For example, VRAM-per-dollar, limiting to Nvidia, and a minimum performance score allow you to find good ML GPUs. It currently supports Australia, Canada, Ireland, the UK, and the US. The tech stack is Python for data pull and static site generation then Cloudflare Pages for actually serving the site. It updates three times a day, but I could increase…
2024 · gpu-prices.com
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Out of curiosity, I put together a simple website which tracks the prices for a few variations of A100/H100 GPUs by hour broken out between spot/ondemand, form factor and provider. Specifically I was tailoring the tool towards the smaller, emerging providers like runpod, gpulist.ai, lambda labs etc. Anyone have any ideas to expand/refine it?
2024 · computeindex.michaelgiba.com
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This project began when I decided it would be easier to write an autorouter than route a 8000+ net backplane by hand. This is a KiCad plugin with a few different algorithms, the coolest of which is a 'Manhattan routing grid' autorouter that routes along orthogonal traces. The basic idea was to steal an algorithm from FPGA routing and apply it to PCBs. I'm using CuPy for speeding up the routing; CPU-bound is at least 10x slower than the GPU version. This is in a very pre-alpha state, but it does _technically_ work. It's not great by any measure but then again it is an autorouter. I have a…
Oct 2025 · github.com
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Hi HN community, Shen and I created a service for anyone to easily train deep learning model on GPU power harnessed from the crowd. We have completed the first version DeepCluster.io (http://deepcluster.io) and welcome ML researchers to try it out for free! We are enthusiastic of deep learning, but often found training models with GPU instances on AWS very expensive. Meanwhile, some of our friends have idle GPUs that are used to mine cryptos. So we decided to borrow their GPUs for training deep learning model ourselves, and believe this could be a service that benefits other ML…
2019
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We built, saga[1] a layer that lets smaller teams access enterprise GPU discounts through collective buying power. How it works: 1. Aggregate GPU spend across hundreds of ML teams 2. Get enterprise rates through combined volume 3. Pass savings to users, monetize via provider partnerships Technical notes: - Works at billing layer only (no access to code/data) - Supports existing cloud setups or managed GPUs - Private beta running since January, opening more spots for March - Currently seeing ~50% savings on H100s/A100s [1] https://trysaga.ai
2025 · trysaga.ai
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2018 · github.com
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2016 · github.com
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I've been recently working on porting standard C library functions to work on the GPU https://libc.llvm.org/gpu/. A colleague of mine suggested using it to run DOOM, so that's what I did. It runs on both AMD and NVIDIA GPUs and it is completely playable. This works by targeting C code directly for the GPU via cross-compilation in clang, looks something like this https://godbolt.org/z/hh44a6vKr. The LLVM C library will provide the headers, C library functions, and the kernel that calls the main function, so we only need to compile the DOOM source code…
2024 · 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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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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