#340 Steffen Cruz: Training AI Without Data Centres
Episode
46 min
Read time
2 min
Topics
Remote Work, Personal Finance, Fundraising & VC
AI-Generated Summary
Key Takeaways
- ✓Distributed Pretraining Economics: Training large language models through geographically distributed nodes enables cost arbitrage unavailable to centralized data centers. When a facility builds out thousands of GPUs, training costs are fixed at construction. Distributed systems can target surplus energy pockets — such as Icelandic renewable energy available only 12 hours daily — reducing training costs to roughly 10–20% of conventional rates.
- ✓Model Parallelism Architecture: Macrocosmos's IOTA system (Incentivized Orchestrated Training Architecture) splits models into small slivers across nodes rather than running full model copies on each machine. This approach allows training of frontier-scale models — targeting 70 billion parameters by mid-2025 and 100 billion-plus by 2026 — using consumer-grade hardware like Mac minis and CUDA-enabled GPUs.
- ✓Supply-Side GPU Utilization Strategy: Cloud providers and neo-clouds with idle GPU inventory can plug surplus capacity into IOTA's network during rental gaps. Since training commands higher margins than inference token sales, providers earn better returns on underutilized hardware than selling compute at discounted spot rates, creating a direct bottom-line improvement without additional capital expenditure.
- ✓Consumer Passive Income via Train-at-Home: Individuals with idle Mac minis, MacBooks, or consumer GPUs can download a one-click app, set availability windows — for example, 10PM to 6AM — and earn passive income contributing to model training runs. Macrocosmos reports 2,500 app downloads within the first two weeks, with the payout system rewarding participation proportionally to hours of compute contributed daily.
- ✓Blockchain as Coordination Layer, Not Compute: The blockchain in BitTensor functions as an identity registry, synchronization clock, and transparent payout trigger — not as a compute or storage layer. Off-chain tracking records each node's contribution, then pushes verified totals on-chain to trigger token payouts. This architecture allowed Macrocosmos to scale beyond BitTensor's native 256-node limit to support thousands of simultaneous participants.
What It Covers
Steffen Cruz, CTO of Macrocosmos, explains how his company uses BitTensor's blockchain infrastructure to train large language models through distributed compute nodes worldwide, eliminating the need for centralized data centers and enabling cost arbitrage through surplus energy and idle consumer hardware like Mac minis and spare GPUs.
Key Questions Answered
- •Distributed Pretraining Economics: Training large language models through geographically distributed nodes enables cost arbitrage unavailable to centralized data centers. When a facility builds out thousands of GPUs, training costs are fixed at construction. Distributed systems can target surplus energy pockets — such as Icelandic renewable energy available only 12 hours daily — reducing training costs to roughly 10–20% of conventional rates.
- •Model Parallelism Architecture: Macrocosmos's IOTA system (Incentivized Orchestrated Training Architecture) splits models into small slivers across nodes rather than running full model copies on each machine. This approach allows training of frontier-scale models — targeting 70 billion parameters by mid-2025 and 100 billion-plus by 2026 — using consumer-grade hardware like Mac minis and CUDA-enabled GPUs.
- •Supply-Side GPU Utilization Strategy: Cloud providers and neo-clouds with idle GPU inventory can plug surplus capacity into IOTA's network during rental gaps. Since training commands higher margins than inference token sales, providers earn better returns on underutilized hardware than selling compute at discounted spot rates, creating a direct bottom-line improvement without additional capital expenditure.
- •Consumer Passive Income via Train-at-Home: Individuals with idle Mac minis, MacBooks, or consumer GPUs can download a one-click app, set availability windows — for example, 10PM to 6AM — and earn passive income contributing to model training runs. Macrocosmos reports 2,500 app downloads within the first two weeks, with the payout system rewarding participation proportionally to hours of compute contributed daily.
- •Blockchain as Coordination Layer, Not Compute: The blockchain in BitTensor functions as an identity registry, synchronization clock, and transparent payout trigger — not as a compute or storage layer. Off-chain tracking records each node's contribution, then pushes verified totals on-chain to trigger token payouts. This architecture allowed Macrocosmos to scale beyond BitTensor's native 256-node limit to support thousands of simultaneous participants.
Notable Moment
Cruz describes a near-future scenario where a personal AI agent, after completing its assigned tasks by mid-morning, autonomously decides to contribute the machine's idle compute to a training network and earns money before the owner returns home — reframing personal computers as proactive economic participants rather than passive tools.
Episode Transcript
So my name is Stefan Crews. I am, I'm the cofounder and the CTO of Microcosmos. I hold a PhD in subatomic physics in University of British Columbia. So I was a I was a physics researcher for the beginning of my career, and then I pivoted to AI when it became apparent to me that there's a lot of opportunity for scientists that are just about to graduate from their, from their postgraduate studies. And and there's just there's there's an entirely new science sort of being born and developed in real time before us, and I think my decision is one that a lot of other people have made as well where they want to go from, being in what can feel like the sort of the old school machinery of of traditional science in academia where things move slow and your contributions can be very narrow and sparse to becoming a pioneer of this entirely new exciting thing, which is not only abstract and theoretical, but it's being applied in new and interesting ways constantly. So when I saw that, I I I concluded my PhD, and I decided to leap, leap leaped out of academia and into sort of the application of, AI and also physics in in various private sector domains such as manufacturing and systems optimization. Following that, I I discovered BitTensor, which is a very interesting AI, blockchain project. And, ostensibly, the purpose of BitTensor is to allow AI to be developed in a way that is fully democratized and is sort of done in a global a global way with incentives. And I thought that was such a fascinating, proposal that I decided to join the network. I've been within the BitTensor ecosystem for around three years now, and I have been relentlessly experimenting and building within BitTensor, the kind of things that I think would make a lot of sense and would bring a lot of value to the world using a blockchain as a prerequisite tool to enable AI research to be done in various ways and scales that will be very difficult otherwise. Yeah. And specifically well, first, let's talk about BitTensor. But before you began, we were talking about, how there are a few of these blockchain ecosystems, for either training or, or marketing, AI models or AI applications. And I'm familiar with SingularityNET. Bit tensor is much larger. Is that right? Is it the largest of these or one of the largest, and how many are there? I believe so. So bit tensor is actually over a 100 projects under a trench coat, and I think that's also why it feels so encompassing and so large. And this is what is, very interesting about BitTensor is it doesn't seek to solve a specific narrow problem in the AI industry. It's not like trying to solve something like how do we provide people with access to models, or how do we train models, or how do we do …
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“Steffen Cruz, CTO of Macrocosmos, explains how his company uses BitTensor's blockchain infrastructure to train large language models through distributed compute nodes worldwide.”
by Macrocosmos
“Macrocosmos's IOTA system (Incentivized Orchestrated Training Architecture) splits models into small slivers across nodes rather than running full model copies on each machine.”
company
- MacrocosmosBy guest
“Steffen Cruz, CTO of Macrocosmos, explains how his company uses BitTensor's blockchain infrastructure to train large language models through distributed compute nodes worldwide.”
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