Raven Protocol 🐦⬛ @raven_protocol
Raven is developing a distributed network of compute nodes for Artificial Intelligence and Machine Learning. A decentralized computing protocol built on #BNB ravenprotocol.com Hong Kong and Bangalore Joined November 2017-
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In 2012, training ImageNet took 6 days on 1,000 CPUs. In 2025, training GPT-4 scale models takes months on 25,000+ GPUs. Compute demand is outpacing Moore's Law by 10x. We're hitting centralization limits while billions of devices sit idle. Something has to give.
"Nearly 9 in 10 telecom and cloud providers plan to expand or maintain AI infrastructure investments, with the top three growth factors cited as cloud integration, faster GPUs, and network upgrades." Everyone's scaling up. What if we scaled out instead? keysight.com/us/en/about/ne…
By 2028, training the largest AI models will require computing power of entire countries. Or we could rethink the architecture. Instead of concentrating power in mega data centers, harnessing the collective strength of millions of connected devices. Distribution > domination.
"AI data centers could need 68 gigawatts of power capacity by 2027—close to the current total power capacity of the state of California." Building new power grids, or connecting the compute that's already plugged in? rand.org/pubs/research_…
Distribute computing power
gm to everyone who thinks differently about ai computation scale.
Training a single frontier AI model now requires more compute than the entire internet used in 1995. The world's largest supercomputers are getting bigger, but billions of devices sit idle with untapped processing power. Time to rethink the architecture. $RAVEN
"We created three investment scenarios ranging from constrained to accelerated demand. In the first scenario, this would require an estimated $7.9 trillion in capital expenditures by 2030." Or we could just connect the compute that's already out there. mckinsey.com/industries/tec…
gm to everyone except those who don't see the potential in distributed computing.
September kicks off the final quarter of 2025. The AI infrastructure conversation is evolving fast. From "how big can we build?" to "how smart can we distribute?" The shift is happening.
"Just as the world learned by solving distributed computing problems across under-utilized PCs with SETI, we might too be able to put spare cycles to work training large-scale AI models." History repeating itself. Old ideas becoming new breakthroughs. nextplatform.com/2022/02/07/dis…
GM. The infrastructure shift continues.
Distributed by design. Accessible by choice. Powerful by connection. $RAVEN
What's the biggest barrier preventing AI startups from scaling their models? Drop your thoughts below 👇
Every smartphone has enough processing power to contribute to AI model training. 8 billion people. 8 billion potential compute nodes. We're sitting on the world's largest supercomputer. We just haven't connected it yet. $RAVEN
"The shortage of GPUs has been primarily caused by the unprecedented growth of AI tools and services, which require massive amounts of computing power for their development and training." Meanwhile, millions of gaming PCs sit idle 20+ hours daily. blog.spheron.network/how-can-we-man…
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