@raven_protocol

Raven is developing a distributed network of compute nodes for Artificial Intelligence and Machine Learning. A decentralized computing protocol built on #BNB

 Hong Kong and Bangalore
ravenprotocol.com
Joined November 2017
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  • Followers12.8K
395Media
🏆Raven 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/rav…

Raven Protocol Wins the Mozilla Builder Award at the Fix-the-Internet Showcase

Receives grant funding to scale the decentralized compute network and speed up development of the open sourced distribution framework.

medium.com
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1/ The AI compute problem isn’t just supply, it’s fragmentation. 300+ GPU providers, each with different hardware, networking, and software. AI teams struggle to find capacity. Providers struggle to keep GPUs productive. The market is inefficient at every layer.
Hyperbolic
@hyperbolic_labsOct 27
We’ve raised an oversubscribed $30M Series A1 led by @walden_catalyst to build the Global Compute Grid. We’re turning fragmented GPU capacity into one unified platform where AI teams can access, manage, sell, and orchestrate compute. Our mission: make the world’s compute unified, productive and liquid.
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2/ Hyperbolic just raised $30M for a Global Compute Grid: - Unified control plane across heterogeneous GPU clusters - Dynamic pricing to match supply with demand - Programmable compute access for AI agents Compute coordination is the next infra layer. Thats Ravnest’s territory.
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1/ Open-source AI has made powerful models easier to access. Models like GLM-5.3, DeepSeek V4.1, Beam, and Kimi K3 are pushing the frontier. But downloading the weights is only the first step. The harder problem is what comes next. 🧵
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2/ Downloading a 500B model & training it are completely different problems Three walls most open source devs hit - Single-node memory limits - Bandwidth bottlenecks during gradient sync -Node failures mid-run Most frameworks werent built for consumer hardware across real n/ws.
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3/ Ravnest was built for exactly this environment. Distributed training across heterogeneous consumer hardware: data parallelism, model parallelism, and async training in one framework. Open weights are only as useful as the infrastructure that can train them.
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1/ Every transformative technology entered organizations chaotically from the bottom up, before anyone built the habitat for it. PCs, spreadsheets, email, agents. The pattern holds: the chaos is the signal. The habitat comes next.
Marc Andreessen 🇺🇸
@pmarcaOct 26
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For open source AI, that pattern is playing out at the infrastructure layer: - Models accessible - Inference solved: Ollama, vLLM mainstream - Training on your own hardware The chaos is the signal. Ravnest is part of building the habitat.
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1/ Last Week in Open Source AI & Distributed Compute - NVIDIA releases Nemotron 3.5 Super VL, a 120B multimodal model, alongside two open-weight Bolt edge models - llm-d 1.0 launches as a stable Kubernetes-native stack for production-scale LLM inference
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3/ - DeepSeek open-sources DeepInfer, its production inference framework, under Apache 2.0, with support for NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs - OpenCode discloses a breach exposing API keys belonging to 1.4% of Console users with external providers
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4/ - Researchers demonstrate how a malicious AGENTS.md file can trick a coding agent into installing a rogue npm package - Block releases Dessa, a full-duplex speech model trained from human conversation that runs locally on high-end MacBook Pro hardware
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GM, new week. Open-source AI keeps shipping. The real work starts when you move from accessing a model to making it your own.
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1/ Open source models now handle 78% of token volume on major AI gateways. That shift didn't happen gradually. It happened because open models crossed a threshold, good enough capability at a fraction of the cost. For most workloads, the choice became obvious.
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2/ Model access and model ownership are different things. Running an open model via an inference provider is access. Fine-tuning on your own data, training domain-specific variants, that's ownership. Most open source developers are still stuck at access.
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3/ The bottleneck isn't the model anymore. It’s the training infrastructure to actually make it yours. Ravnest distributes training across consumer hardware you already own. No cloud invoice, no GPU queue, no datacenter access required. That's the gap it was built to fill.
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1/ This chart tells you everything about where AI infrastructure costs are heading. US semiconductor producer prices surged +26% YoY in September: - 6th consecutive 25%+ YoY increase - Up 31% since July 2025 - Declined every year from 1994–2021 Historic territory.
The Kobeissi Letter
@KobeissiLetterOct 24
Semiconductor prices are skyrocketing in the US: Producer prices for semiconductor and other electronic component manufacturing surged +26% YoY in September, their 4th-largest increase on record. This also marks their 6th consecutive YoY increase of at least +25%. Since July 2025, the Producer Price Index for semiconductor and other electronic component manufacturing has risen +31%, to its 2nd-highest level since January 2007. To put this into perspective, the index declined every year from 1994 to 2021. Meanwhile, producer prices for electronic components and accessories soared +27% YoY last month, also marking their 6th consecutive YoY increase of at least +25%. The AI Revolution is driving historic technology inflation.
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2/ AI demand is putting pressure on semiconductor supply. Expanding production capacity takes time & the cost of new hardware remains a challenge for teams building & training AI models. As compute demand grows, relying on ever more expensive hardware becomes harder to sustain.
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3/ Training on consumer hardware that's already paid for isn't just practical anymore. As new hardware costs keep rising, the economics of distributed training across existing nodes get stronger, not weaker. That's the environment Ravnest was built for.
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gm, the open source AI stack keeps shipping. good day to be building on it.
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1/ Building a custom training pipeline on distributed infra usually means one thing: your research adapts to the framework, not the other way around. Custom loss functions, non-standard architectures, experimental optimizers ,most distributed frameworks treat them as edge cases.
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2/ The result: researchers spend more time working around the distributed layer than running experiments. Every unconventional training approach means new workarounds. The infrastructure that should be invisible becomes the biggest obstacle.
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3/ Ravnest’s extensible trainer API is designed to get out of the way. Bring your own training logic. Ravnest wraps the distributed layer: matchmaking, async pipeline, fault tolerance, gradient compression; around it automatically. Your architecture. Distributed by default.
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