Many people don't know that 75% of $ZERO stakers auto-compound their rewards.
To bring more attention to this I've added 3 more charts to the data page.
A total 3.21M $ZERO has been bought back so far by stakers that auto-compound.
data.c0mpute.ai
The c0mpute roadmap is live!
With this roadmap we want to clarify what we're building for and how far along we are. You can check progress live.
c0mpute.ai/roadmap
6/ We ran a test where we kill -9 the coordinator mid-generation, four speculative chunks in flight.
A replacement picked up the same warm ring and finished a full request 17 seconds later, without reloading any of the 115GB of weights.
5/ Every stage signs a hash chain over the computation it actually ran. A node that skips layers fails the request and doesn't get paid.
We measured what this protection costs: 0.05ms on an 11.7ms stage span. About 0.4%.
4/ Something we didn't expect: all five GPUs benchmark identically, yet some stages ran the same layers 4x slower.
It came down to the host CPU and how loaded the box was. A forward pass launches hundreds of small kernels, and slow CPUs launch them slowly.
3/ We tried the obvious latency fix, keeping several speculative chunks in flight, and it dies on reasoning: one rejected token throws away everything behind it.
You need ~80% per-token acceptance before it pays off. The best drafter we know of gets 74% on novel text.
2/ Each decode step travels through all five machines and back before the next one can start. Over real internet links that round trip costs ~350ms, so plain decoding tops out near 5 tok/s.
We measured exactly that, flat, on every workload we tried.
1/ We published our first technical report today.
We ran a 229B model split across five consumer GPUs in five countries over the public internet and measured 12.6 tok/s interactive, 194 tok/s batched.
With cryptographic receipts on every request.
doi.org/10.5281/zenodo.21178…
The bottleneck of local ai is the ability of running frontier models on affordable consumer hardware.
Now imagine connecting all the idle GPU's to one network and running sharded inference.
Swarms powering frontier models such as GLM-5.2, that's the goal of @c0mputeAI.
JUST IN: 🇺🇸 US tells OpenAI to limit GPT 5.6 release while the government reviews it, The Information reports.
CEO Sam Altman: "we've made clear to the US government that this is not our preferred long term model."
Some progress updates on the shard engine:
- 28.2 tok/s @ 100k context (copy/retrieval)
- added mid-gen healing
- 30k context prefill down to 60.8s from 153.3s
The next goal is turning c0mpute into a beta-net for gpt-oss-120b, where everyone can join & leave get paid.
You can now buy decentralized, private and uncensored inference from c0mpute on UsePod.ai!
If you're a staker you can sell your daily inference allowance by making a special free inference API key and connecting it to POD.
UsePod × c0mpute: private, uncensored inference is live
We’ve integrated c0mpute, a decentralized inference network, into the UsePod marketplace. If you want models that don’t refuse, don’t store your prompts, and run on a distributed worker network, there’s a model for that!
Find them under the new Uncensored filter on the marketplace. Same drop-in OpenAI-compatible API, same USDC balance, just choose one of the new models.