Raven is developing a distributed network of compute nodes for Artificial Intelligence and Machine Learning. A decentralized computing protocol built on #BNB
Joined November 2017
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5 Nov 2020
πRaven wins the @mozillabuilders Award at the Fix-the-Internet showcase.
Recognition by the top open source organization in the world that Raven Protocol is a core infrastructure project.
We demonstrated best traction and commitment to fix the internet!
medium.com/ravenprotocol/ravβ¦
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gm to those who know the AI race is no longer about models, it's about who can afford to serve them.
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Centralized training locks workloads to one provider. Pricing changes, capacity limits, outages: all outside your control.
Ravnest runs across distributed consumer nodes. No single provider dependency. Workloads move where hardware exists.
Training stays in your hands.
$RAVEN
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Hyperscaler infrastructure costs flowing downstream to enterprises: tiered pricing, premium AI bundles, usage-based billing, tighter consumption controls.
Ravnest trains across distributed hardware. No downstream cost pressure, no vendor billing cycles.
informationweek.com/machine-β¦
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gm to those who treat infrastructure as strategy, not plumbing.
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Cloud bills scale with every training run. Bigger model, longer session, higher invoice.
Ravnest trains across consumer hardware already online. Workload distributed across existing nodes: no cloud invoice, no surprise billing, no vendor dependency.
$RAVEN
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AI infrastructure race now financed with debt, $17.5B borrowed to fund cloud capacity expansion. The race is about who can afford to serve them at scale.
Ravnest coordinates existing distributed hardware. No debt financing required to train.
techstartups.com/2026/06/11/β¦
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gm to those who know utilization and efficiency matter more than deploying more hardware.
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Data parallelism replicates the full model everywhere. Memory-intensive, bandwidth-heavy, breaks on large models.
Ravnest combines data parallelism and model parallelism. Model splits across nodes, data splits across clusters
Trains models too large for any single node.
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Geopolitical conflict now disrupting AI supply chains beyond GPUs, circuit boards, optical interconnects, voltage regulation systems & cooling components all affected. Every layer of the stack exposed.
Ravnest trains across distributed consumer hardware.
datacenterknowledge.com/dataβ¦
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Inference compute needs already 100x beyond early LLM requirements & still growing. Most teams scaling AI hit the same wall: cloud bills, GPU queues, capacity limits.
Ravnest distributes training across consumer hardware already online.
Compute exists. Just needs coordination.
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Scaling global datacenter infrastructure may require $7 trillion by 2030. GPU architectures refresh every 18-24 months, making it nearly impossible to keep up with AI infrastructure cycles.
Ravnest coordinates existing distributed hardware.
rcrtech.com/category/ai-infrβ¦
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gm to those who don't beg, queue, or compromise for compute.
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Enterprises moving AI to production face one consistent problem, GPU costs scaling faster than budgets.
The fix isn't always more GPUs.
Ravnest coordinates heterogeneous consumer hardware already online. Workloads distributed across existing capacity.
$RAVEN
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AI pilot-to-production conversion hit 31% in Q2 2026, nearly doubling Q1's 18%. AI moving from experimentation to real deployment at scale. Compute demand following the same curve.
Ravnest trains across distributed hardware ready for that scale.
aiapps.com/blog/ai-news-breaβ¦
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Most training jobs are written for one environment. Move to different hardware and the entire setup breaks: dependencies, roles, configurations all need rebuilding.
Ravnest runs a single common script across every node in the cluster.
Environment changes, script doesnt.
$RAVEN
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Nearly $100B invested in sovereign AI compute by end of 2026. Nations racing to secure strategic independence in AI capabilities.
Distributed training isnt just efficient, its resilient. Ravnest coordinates nodes across existing hardware globally.
kavout.com/market-lens/2026-β¦
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Which layers go where? Wrong answer wastes the entire cluster.
Ravnest profiles every node; hardware specs, network throughput, latency - before training starts. Similar performers grouped together. Bottlenecks eliminated upfront.
$RAVEN
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Optical transport lead times stretching beyond 12 months. Market growing 16% in 2026 but supply can't keep up. Networking now the newest bottleneck in the datacenter stack.
Ravnest routes around congestion using adaptive paths on consumer networks.
rcrtech.com/category/ai-infrβ¦
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Gradient sync across large clusters bottlenecks on the slowest link.
Ravnest compresses gradients before transmission. Bandwidth drops, slow links stop dictating cluster speed.
Built for consumer internet, not datacenter fabric.
$RAVEN
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gm to those who activate infrastructure instead of ordering it.
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