This looks like a startup company. Why shouldn't it have a name and logo?
iTokio 6 hours ago [-]
It’s interesting that it does something useful (training a LLM) without trust and in a decentralized way.
Maybe this could be used as proof of work? To stop wasting computing resources in crypto currencies and get something useful as a byproduct.
_ink_ 3 hours ago [-]
I read an argument, that proof of work needs to be useless and wasteful. If it would produce value in itself it would make 51% attacks more economic and thus the currency less secure.
throwanem 1 hours ago [-]
Sure. The whole point of "proof of work" is to show (prove) you've lost energy to heat (work). That's what makes it costly and thus an honest signal.
The model breaks where work can be counterfeited (usually impossible) or where energy prices go to zero, which is why "bitcoin colonialism" was briefly a thing last decade. Much of bitcoin's design, this aspect also, is intended to protect against the bare-fanged, red-eyed money weasels it was also designed to attract.
ucha 1 hours ago [-]
It needs to not have economic value but it doesn't necessarily need to be useless and wasteful.
Geee 3 hours ago [-]
No, this process doesn't produce "proof of work", i.e. verifiable proofs that energy has been used.
2 hours ago [-]
fastball 5 hours ago [-]
The emphasis is indeed on "without trust" – as far as I can tell this project is unable to verify whether the decentralized training nodes are contributing productively.
Without the ability to validate that training compute is heading in the globally desired direction, it is unlikely you could use it as the foundation of a (sound) cryptocurrency.
mentalgear 4 hours ago [-]
The reward model could be used as a validation/reward for the client. Give the same nodes the same inferences to make, and the one with the highest reward (those could be short, or even partially calculated long-term) will also get the "currency" reward.
k__ 53 minutes ago [-]
Arweave and Filecoin use PoW algorithms that prove something useful.
mentalgear 4 hours ago [-]
That would be indeed a very promising way of FINALLY making cryptocurrency useful!
proof_by_vibes 5 hours ago [-]
There could be merit to this. Proofs are generally computationally hard, so it's possible that a currency could be created by quantifying verification.
littlestymaar 5 hours ago [-]
> To stop wasting computing resources in crypto currencies and get something useful as a byproduct.
Bitcoin is the only major cryptocurrency that still use proof of work today (others are either using “proof of stakes” or are “Layer 2” chains), and due to its (relative lack of) governance structure, it's very unlikely to ever change.
Thomashuet 3 hours ago [-]
Summary: We've use the most complexest, buzzwordiest training infrastructure to increase the performance of our base model by a whopping 0.5% (±1%).
Weryj 54 minutes ago [-]
But this isn’t about the performance, the infrastructure is the product here.
3abiton 6 hours ago [-]
This is rather exciting! I see the future of Co-op models made by a community of experts on a specific field that would still allow them to be competitive with "AI monopolies". Maybe not all hope is lost!
Does this have anything to do with The Metamorphosis Of Prime Intellect, or did they just abuse the name and the cover art?
arthurcolle 7 hours ago [-]
Prime Intellect is a grabby AI :)
refulgentis 8 hours ago [-]
I guess I'm bearish?
It's not that they trained a new model, but they took an existing model and RL'd it a bit?
The scores are very close to QwQ-32B, and at the end:
"Overall, as QwQ-32B was already extensively trained with RL, it was difficult to obtain huge amounts of generalized improvement on benchmarks beyond our improvements on the training dataset. To see stronger improvements, it is likely that better base models such as the now available Qwen3, or higher quality datasets and RL environments are needed."
fabmilo 8 hours ago [-]
The interesting delta here is that this proves that we can distribute the training and get a functioning model. The scaling factor is way bigger than datacenters
comex 7 hours ago [-]
But does that mean much when the training that produced the original model was not distributed?
refulgentis 7 hours ago [-]
The RL, not the training. No?
itchyjunk 5 minutes ago [-]
RL is still training. Just like pretraining is still training. SFT is also training. This is how I look at it. Models weights are being updated in all cases.
christianqchung 7 hours ago [-]
Third party fine tuned open weighted LLMs tend to be good at a handful of benchmarks, but parity or lower on others compared to the original model. There are some exceptions like Nvidia's Nemotron series, but the differences generally are so small as to be imperceptible. Deepseek released finetunes of several Qwen and Llama models alongside R1, and while they were better in some select (mostly math) and coding domains, there were problems resulting from fine tuning that didn't result in them overtaking the original models in usage.
cess11 56 minutes ago [-]
Seems that's mostly a byproduct from working on the core business idea, GPU arbitrage.
esafak 9 hours ago [-]
How are they ensuring robustness against adversarial responses?
nsingh2 8 hours ago [-]
From the article, seems like TOPLOC:
> based on top of novel components such as TOPLOC, which verifies rollouts from untrusted inference workers
The checksum is validated by redoing the computation, but making use of the fact that you already have the entire response to enable greater parallelism than when generating it one token at a time.
schneehertz 8 hours ago [-]
I used to have an idea related to science fiction novels that artificial intelligence could aggregate computing power through the network to perform ultra-large-scale calculations, thereby achieving strong artificial intelligence.
Reality will also develop in this way, which is very interesting
mountainriver 8 hours ago [-]
Awesome work this team is doing. Globally distributed MoE could have real legs
ndgold 8 hours ago [-]
Pretty badass
quantumwoke 8 hours ago [-]
Wonder what the privacy story is like. Enterprises don't usually like broadcasting their private data across a freely accessible network.
bjt12345 7 hours ago [-]
A strong use case here for quantum-safe encryption.
jumploops 8 hours ago [-]
Congrats to the team on the launch!
Personal story time: I met a couple of their engineers at an event a few months back. They mentioned they were building a distributed training system for LLMs.
I asked them how they were building it and they mentioned Python. I said something along the lines of “not to be the typical internet commenter guy, but why aren’t you using something like Rust for the distributed system parts?”
They mumbled something about Python as the base for all current LLMs, and then kinda just walked away…
From their article:
> “Rust-based orchestrator and discovery service coordinate permissionless workers”
Glad to see that I wasn’t entirely off-base :)
Havoc 4 hours ago [-]
Given the latencies at play python did probably make more sense though
Maybe this could be used as proof of work? To stop wasting computing resources in crypto currencies and get something useful as a byproduct.
The model breaks where work can be counterfeited (usually impossible) or where energy prices go to zero, which is why "bitcoin colonialism" was briefly a thing last decade. Much of bitcoin's design, this aspect also, is intended to protect against the bare-fanged, red-eyed money weasels it was also designed to attract.
Without the ability to validate that training compute is heading in the globally desired direction, it is unlikely you could use it as the foundation of a (sound) cryptocurrency.
Bitcoin is the only major cryptocurrency that still use proof of work today (others are either using “proof of stakes” or are “Layer 2” chains), and due to its (relative lack of) governance structure, it's very unlikely to ever change.
./llama.cpp/llama-cli -hf unsloth/INTELLECT-2-GGUF:Q4_K_XL -ngl 99
Also it's best to read https://docs.unsloth.ai/basics/tutorial-how-to-run-qwq-32b-e... on sampling issues for QwQ based models.
Or TLDR, use the below settings:
./llama.cpp/llama-cli -hf unsloth/INTELLECT-2-GGUF:Q4_K_XL -ngl 99 --temp 0.6 --repeat-penalty 1.1 --dry-multiplier 0.5 --min-p 0.00 --top-k 40 --top-p 0.95 --samplers "top_k;top_p;min_p;temperature;dry;typ_p;xtc"
It's not that they trained a new model, but they took an existing model and RL'd it a bit?
The scores are very close to QwQ-32B, and at the end:
"Overall, as QwQ-32B was already extensively trained with RL, it was difficult to obtain huge amounts of generalized improvement on benchmarks beyond our improvements on the training dataset. To see stronger improvements, it is likely that better base models such as the now available Qwen3, or higher quality datasets and RL environments are needed."
> based on top of novel components such as TOPLOC, which verifies rollouts from untrusted inference workers
https://github.com/PrimeIntellect-ai/toploc
At a glance it looks like something akin to a computing a checksum that's locality sensitive, so it's robust to floating point errors, etc.
What's to stop someone from sending bad data + a matching bad checksum?
The checksum is validated by redoing the computation, but making use of the fact that you already have the entire response to enable greater parallelism than when generating it one token at a time.
Personal story time: I met a couple of their engineers at an event a few months back. They mentioned they were building a distributed training system for LLMs.
I asked them how they were building it and they mentioned Python. I said something along the lines of “not to be the typical internet commenter guy, but why aren’t you using something like Rust for the distributed system parts?”
They mumbled something about Python as the base for all current LLMs, and then kinda just walked away…
From their article: > “Rust-based orchestrator and discovery service coordinate permissionless workers”
Glad to see that I wasn’t entirely off-base :)