Read the discussion on HN. A full-thread summary is not available yet.
exo-explore
exo
Python2024–2026
exo-explore / exo
exo
Run frontier AI locally.
20Distinct people
25Mentions
0Explicit recommenders
48.4KGitHub stars
What to know before exploring
What it is
Run frontier AI locally.
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.
Mentions over time
HN mentions by month25 dated mentions
Observed mentions: 2024-07-16 02:55:11 UTC to 2026-10-13 13:42:34 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Jul 2024 | 2024-07-01 00:00:00 UTC | 2024-08-01 00:00:00 UTC | 7 | 1 | 6 |
| Aug 2024 | 2024-08-01 00:00:00 UTC | 2024-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2024 | 2024-09-01 00:00:00 UTC | 2024-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2024 | 2024-10-01 00:00:00 UTC | 2024-11-01 00:00:00 UTC | 1 | 0 | 1 |
| Nov 2024 | 2024-11-01 00:00:00 UTC | 2024-12-01 00:00:00 UTC | 1 | 0 | 1 |
| Dec 2024 | 2024-12-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 1 | 0 | 1 |
| Jan 2025 | 2025-01-01 00:00:00 UTC | 2025-02-01 00:00:00 UTC | 2 | 0 | 2 |
| Feb 2025 | 2025-02-01 00:00:00 UTC | 2025-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2025 | 2025-03-01 00:00:00 UTC | 2025-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2025 | 2025-04-01 00:00:00 UTC | 2025-05-01 00:00:00 UTC | 1 | 0 | 1 |
| May 2025 | 2025-05-01 00:00:00 UTC | 2025-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2025 | 2025-06-01 00:00:00 UTC | 2025-07-01 00:00:00 UTC | 1 | 0 | 1 |
| Jul 2025 | 2025-07-01 00:00:00 UTC | 2025-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2025 | 2025-08-01 00:00:00 UTC | 2025-09-01 00:00:00 UTC | 1 | 0 | 1 |
| Sep 2025 | 2025-09-01 00:00:00 UTC | 2025-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2025 | 2025-10-01 00:00:00 UTC | 2025-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2025 | 2025-11-01 00:00:00 UTC | 2025-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2025 | 2025-12-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 4 | 1 | 3 |
| Jan 2026 | 2026-01-01 00:00:00 UTC | 2026-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2026 | 2026-02-01 00:00:00 UTC | 2026-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2026 | 2026-03-01 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 1 | 0 | 1 |
| Apr 2026 | 2026-04-01 00:00:00 UTC | 2026-05-01 00:00:00 UTC | 1 | 0 | 1 |
| May 2026 | 2026-05-01 00:00:00 UTC | 2026-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2026 | 2026-06-01 00:00:00 UTC | 2026-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2026 | 2026-07-01 00:00:00 UTC | 2026-08-01 00:00:00 UTC | 1 | 0 | 1 |
| Aug 2026 | 2026-08-01 00:00:00 UTC | 2026-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2026 | 2026-09-01 00:00:00 UTC | 2026-10-01 00:00:00 UTC | 1 | 0 | 1 |
| Oct 2026 | 2026-10-01 00:00:00 UTC | 2026-11-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
25 sourcesGEgempir·Oct 13, 2026
Interesting, so this is basically Apple's version of exo ? https://github.com/exo-explore/exo
In A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon DevicesDoesn't do any actually clustering like https://github.com/exo-explore/exo does.
In Nvidia PAIR turns your household's idle PCs into a local AI clusterCheck out exo if you haven’t already https://github.com/exo-explore/exo
In Using a Gaming PC's RTX 5070 from a separate Linux workstationlol bro there is already a big OSS project called exo: https://github.com/exo-explore/exo
In Exo: Claude Code for your Inboxhttps://github.com/exo-explore/exo
I keep hearing about it but unfortunately I myself only have one mac and nvidia GPUs and those can’t cluster together :/
In A guide to local coding modelsTheres some technical implementations that makes it more efficient like EXO [1]. Jeff Geerling recently did a review on a 4 MAC Studio cluster with RDMA support and you can see that EXO has a noticeable advantage [2].
[1] https://github.com/exo-explore/exo
[2] https://www.youtube.com/watch?v=x4_RsUxRjKU
In Big GPUs don't need big PCsExo-Labs is an open source project that allows this too, pipeline parallelism I mean not the latter, and it's device agnostic meaning you can daisy-chain anything you have that has memory and the implementation will intelligently shard model layers across them, though its slow but scales linearly with concurrent requests.
Exo-Labs: https://github.com/exo-explore/exo
In macOS 26.2 enables fast AI clusters with RDMA over ThunderboltGreat effort, a strong self-hosting community for LLMs is going to be similarly important as the FLOSS movement imho. But right now I feel the bigger bottleneck is on the hardware side rather than software. The amount of fast RAM that you need for decent models (80b+ params) is just not something that's commonly available for consumer hardware right now, not even gaming machines. I heard that Macs (minis) are great for the purpose, but you don't really get them with enough RAM or at prices that don't really qualify as consumer-grade anymore. I've seen people create home clusters (eg using Exo [0]), but I wouldn't really call it practical (single digit token/sec for large models, and the price isn't exactly accessible either). Framework (the modular laptop company) has announced a desktop that can be configured up to 128GB unified RAM, but
Read the rest
it's still going to come in at around 2-2.5k depending on your config.
[0] https://github.com/exo-explore/exo
Just ordered a $12k mac studio w/ 512GB of integrated RAM.
Can't wait for it to arrive and crank up LM Studio. It's literally the first install. I'm going to download it with safari.
LM Studio is newish, and it's not a perfect interface yet, but it's fantastic at what it does which is bring local LLMs to the masses w/o them having to know much.
There is another project that people should be aware of:
https://github.com/exo-explore/exo
Exo is this radically cool tool that automatically clusters all hosts on your network running Exo and uses their combined GPUs for increased throughput.
Like HPC environments, you are going to need ultra fast interconnects, but it's just IP based.
In MCP in LM StudioThere are projects working on distributed LLMs, such as exo[1]. If they can crack the distributed problem fully and get performance it’s a game changer. Instead of spending insane amounts on Nvidia GPUs, can just deploy commodity clusters of AMD EPYC servers with tons of memory, NVMe disks, and 40G or 100G networking which is vastly less expensive. Goodbye Nvidia AI moat.
[1] https://github.com/exo-explore/exo
In Microsoft researchers developed a hyper-efficient AI model that can run on CPUsYou can check Exo out:
https://github.com/exo-explore/exo
It's a project designed to run a large model in a distributed manner. My need for GPU is to run my own machine learning research pet project (mostly evolutionary neuron network models for now), and it's a bit different from inferencing needs. Training is yet another different story.
But yeah, I agreed. I think machine learning should be distributed more in the future.
In Nvidia GPU on bare metal NixOS Kubernetes cluster explainedExolabs claims they can distribute the compute over many machines to use memory in aggregate: https://github.com/exo-explore/exo
Maybe there is enough memory in many machines.
In DeepSeek-R1This bottleneck right here is why Open Source is presented with a golden plate opportunity to lead the training of cutting edge models.
Federated learning breaks the barrier to entry and expands the ecosystem allowing more participants to share compute and/or datasets for small players to train models.
DiLoCo introduced by Douillard minimizes communication overhead by averaging weight updates. What this article misses though is that despite this, each GPU in the distributed cluster still needs to have enough VRAM to load the entire copy of the model to complete the training process. That's where DisTrO comes in which even reduces further the inter-GPU communication using a decoupling technique (DeMo) that only shares the fast moving parts of the optimizer across the GPU cluster.
>And what if the costs could drop further still? The dream f
Read the rest
or developers pursuing truly decentralised ai is to drop the need for purpose-built training chips entirely. Measured in teraflops, a count of how many operations a chip can do in a second, one of Nvidia’s most capable chips is roughly as powerful as 300 or so top-end iPhones. But there are a lot more iPhones in the world than gpus. What if they (and other consumer computers) could all be put to work, churning through training runs while their owners sleep?"
This aligns with DisTrO techniques because, according to them it could also allow consumer devices like Desktop Gaming PCs to join the compute cluster and share workloads. Besides there's also an open-source implementation called exo that allows models to be split among idle local devices but it's only limited to inference.
Again might still be relevant since in the article it mentions that DiLoCo was able to make the model respond better when faced with instruction prompts or reasoning questions never encountered during pre-training. And Arthur seems to think test-time training will make his approach become the norm.
sources:
DisTrO: https://github.com/NousResearch/DisTrO
DeMo: https://arxiv.org/pdf/2411.19870
Exo: https://github.com/exo-explore/exo
Showing the 15 most recent of 25 sources. Enable JavaScript to search and browse every indexed mention.