The Web3 + AI Daily #62
While everybody's talking about the agentic economy, blockchains also power decentralized AI training on commodity hardware, including smartphones.
Hello, everyone, and welcome to all newly joined subscribers!
This is The Web3 + AI Daily, your definitive guide to the world of Decentralized AI (DeAI/dAI)! As crypto people head to Cannes for EthCC, where they’ll discuss AI agents at length, I want to zoom in on other compelling examples of why AI needs blockchains. And why crypto may be the only path to democratizing access to AI.
Thank you for being here! Let’s dive in.
What’s Hot in Web3 + AI?
Templar Performs Largest Decentralized LLM Pre-Training Run in History
Covenant AI / Templar, subnet 3 of the Opentensor Foundation / Bittensor decentralized AI ecosystem, has recently released the largest foundation model pre-trained in a completely decentralized and permissionless manner.
Covenant-72B is a 72-billion-parameter large language model (LLM) trained across 70+ contributors using standard hardware on open internet infrastructure.
The model achieved a 67.1 MMLU score (popular benchmark for evaluating LLMs), thus ranking in the same performance range as Meta’s LLaMA 2 70B.
Why is this so important? There are several noteworthy reasons:
Decentralized AI training at a scale is possible: A couple of years ago, training an LLM in a decentralized fashion, using a blockchain-coordinated network of nodes, was deemed not just exorbitantly expensive and energy intensive, but simply impossible. Now, Templar proves it’s doable.
Decentralized AI training is a viable alternative to OpenAI, Google, Anthropic, Meta: Currently, the conversation around AI revolves predominantly around the ability of major labs to borrow billions of dollars to build data centers, while they haven’t yet generated any profit from the technology they’re building. It turns out there’s an alternative nobody is talking about.
Covenant-72B is trained by a network of nodes using commodity hardware. No billion-dollar data centers and no tech behemoths in sight. As 0xSammy wrote:
It’s no wonder that Jack Clark, co-founder of Anthropic, has stated that Covenant-72B is challenging the political economy of AI:
3. Building AI is not just for the tech giants: Recently, the AI race has made it increasingly difficult for independent researchers and smaller labs to access the computing power they need. Cloud services and specialized AI data centers have become so expensive that only billion-dollar corporations can realistically afford them. The result? Progress in AI research and development is now concentrated in the hands of just a handful of companies.
The only meaningful efforts to democratize AI development are currently emerging from the crypto space. Covenant-72B was trained in a completely permissionless and transparent way, where anyone interested could have joined:
4. Participation generates income: Think of Templar, and Bittensor as a whole, as a crowdsourcing platform, but instead of micro donations, it collects computing resources. It allows anyone possessing heavy-duty graphics cards (GPUs) to contribute their computing power to help train a shared, global AI model. Yet, here, contributors gain direct monetary remuneration for a job well done.
That means that anyone with a gaming computer, anywhere on the planet Earth, can lend their idle computing resources and earn passive income. An income that in many parts of the world, has the potential to be truly life-changing.
5. Performance doesn’t have to suffer: Covenant-72B delivers a performance competitive with models trained in centralized data centers - a 67.1 MMLU score, close to the one reached by Meta’s LLaMA 2 70B.
And yes, I should emphasize that Covenant-72B is a bit outdated compared to the models major labs are releasing these days:
However, what’s particularly impressive and exciting about Covenant-72B is the fact that a distributed network of peers, each running 8×B200 GPUs, has trained a model that performs similarly to the one trained by the seventh* richest company in the world (*at the time of writing).
6. Covenant-72B is completely open-source: Instead of locking the model behind a paid API wall, Templar released all the model weights and checkpoints under an open-source Apache 2.0 license for anyone to use.
That’s just another piece of evidence that the Web3 space offers fertile ground for AI experimentation.
Tether AI Enables AI Training and Inference on Smartphones
QVAC, the AI-focused arm of stablecoin issuer Tether.io, launched a new version of its QVAC Fabric, marking a shift from centralized, cloud-based AI to local, on-device AI development. Instead of relying on expensive data centers, the framework allows developers to run, train, and fine-tune large language models directly on everyday hardware like laptops, consumer GPUs, and even smartphones.
QVAC Fabric LLM challenges a long-standing assumption at the heart of modern artificial intelligence: that training and customizing powerful models must be confined to large, centralized data centers. QVAC Fabric LLM is built around a local-first, privacy-first philosophy, enabling individuals and organizations to fine-tune large language models directly on their own devices, using the hardware they already trust and control.
How was that possible? The key breakthrough is bringing fine-tuning, especially via efficient methods like LoRA, to edge devices, making AI personalization far more accessible. Tasks that once required high-end infrastructure can now be done locally, often with just a few commands.
This approach works across all major platforms (mobile and desktop), enabling a truly cross-platform, decentralized AI ecosystem, and eliminates cloud dependency.
QVAC Fabric LLM represents a turning point by democratizing AI development and personalization, shifting power from big tech and data centers to individuals and their own devices. And most importantly, this progress doesn’t come at the expense of performance:
Importantly, broader hardware access does not come at the cost of model quality. Models trained using QVAC Fabric LLM were evaluated against industry-standard benchmarks. Across multiple benchmarks, including biomedical accuracy tasks, performance was on par with the industry standard (PyTorch) and, in some cases, marginally better. In other evaluations, results were effectively equivalent, demonstrating that hardware-agnostic, on-device fine-tuning can match established training standards.
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