
Mesh LLM
Pool compute to run powerful open models
Mesh LLM launched on April 3, 2026 with 96 votes, #439 of 21,248 launches that month and more than 95% of that year's launches. The site was not reachable when checked in September 2026.
What it does
Turn spare capacity into an auto-configured p2p inference cloud. Serve many models, access your private models from anywhere, or share compute with others, let your agents collaborate p2p.
Does a similar job
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General ComputeMay 2026 · generalcompute.com · ▲315AI models that run on an inference cloud optimized for speed
SelfHostLLM2025 · selfhostllm.org · ▲134A simple calculator that estimates how many concurrent requests your GPU can handle for a given LLM, with shareable results.
- WMWe made glhf.chat – run almost any open-source LLM, including 405B2024 · glhf.chat · ▲161
Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…

Cloud World ModelJun 2026 · cloudworldmodel.ai · ▲172How we built native x402 payable API support so AI agents can run simulations and pay per request — no human-in-the-loop billing setup required.
- RLRunning LLMs in one line of Python without Docker2023 · lepton.ai · ▲68
Hello Hacker News! We're Yangqing, Xiang and JJ from lepton.ai. We are building a platform to run any AI models as easy as writing local code, and to get your favorite models in minutes. It's like container for AI, but without the hassle of actually building a docker image. We built and contributed to some of the world's most popular AI software - PyTorch 1.0, ONNX, Caffe, etcd, Kubernetes, etc. We also managed hundreds of thousands of computers in our previous jobs. And we found that the AI software stack is usually unnecessarily complex - and we want to change that. Imagine if you are a…
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