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Centralized AI inference runs on one provider. NEAR AI's confidential inference draws from multiple compute partners distributed across regions instead of a single data center. No KYC required. No visibility into what you're running. Intelligence owned by the user.

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NEAR Protocol

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Lightchain AI – Decentralized Inference Demo 🍂🥧 + Blockchain Rewards In this video, we showcase how Lightchain AI’s decentralized inference engine (AIVM + PoI + Federated Learning) runs a real-world prompt “What is the best way to make pumpkin pie for thanksgiving dinner?” 💰 Blockchain Rewards in Action This demo doesn’t just show inference — it shows how inference providers are rewarded for their work. The provider that processed this prompt was automatically rewarded on the Lightchain testnet. 🍁 Why It Matters Unlike centralized AI, Lightchain AI distributes inference across a global network of participants. Nodes earn blockchain rewards for providing compute, ensuring a trustless, transparent, and economically sustainable ecosystem. With pay-to-prompt mechanics and DAO governance, Lightchain AI creates a fair AI economy for all. 🌐 Learn more at-
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TON 💎

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Weekend. The city slows down. OptimAI Network doesn’t. A query enters OptimAI Search.🔍 It doesn’t hit a single server. It fans out across decentralized nodes. Fetch. Cross-validate. Structure. Proof anchored. No black box ranking farm. No hidden data silos. Just distributed compute turning raw web data into verifiable intelligence for agents. Weekend for humans. Compounding for infrastructure. The future of search is not louder. It’s decentralized. 👉

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AI is rapidly evolving, and it’s reshaping how and where computing happens. From data center infrastructure to AI systems in the physical world, to the future of personal AI devices, intelligence is being distributed across a new compute landscape. At the edge, more agentic experiences are starting to show up, expanding what devices need to do. Tomorrow in New York, we’ll share what Qualcomm is building across it and how it will drive our next phase of growth. Stay tuned for #QCOMInvestorDay.

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Think of OptimAI as a living network - hundred thousands of nodes sensing the web, computing knowledge, verifying truth. Lite Nodes → surface signals & activity Edge Nodes → compute at the edge, background mining Core Nodes → crawl, embed, index, inference, validate & serve workloads Distributed across 179 countries, approaching 800,000 total installations and more than 6,000 Core Nodes online. Every node strengthens collective intelligence. Every contribution expands the data layer powering Agentic AI. This is AI not controlled, but co-created. Built for the People, by the People. Start your node and take part in the next era of intelligence:

OptimAI Network

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70,000 Phones, One AI Agent — The World's Largest Edge AI Fleet Runs on Hermes We turned 70,000 phones into a shared AI compute network. Any device owner contributes idle compute. Any developer taps distributed inference at a fraction of cloud cost. Not a concept. Not a whitepaper. 70K devices online today. The problem: orchestrating a shared network of heterogeneous edge devices — different chipsets, different memory, different thermal profiles, different owners — is a coordination nightmare no human team can handle manually. So we gave the network a brain: Nous Research Hermes Agent. Hermes connects to 16 MCP servers and runs 24/7: 🔬 Research Loop — Tracks every breakthrough in on-device inference: quantization (GPTQ/AWQ/GGUF), speculative decoding on mobile SoCs, federated learning protocols. Auto-imports papers into NotebookLM. 36 research topics, zero manual curation. 🌐 Network Intelligence — Monitors device availability, compute capacity, and workload distribution across the shared fleet. Surfaces bottlenecks before they cascade. 🧬 Tech Tree Optimizer — Maps the full optimization frontier: from KV-cache compression to on-device LoRA to peer-to-peer model sharding. Hermes autonomously identifies which research paths unlock the most network-wide throughput gains. The result: a self-improving shared compute network. Research compounds daily. The fleet gets smarter without human intervention. Cloud AI scales with money. We scale with people. #HermesHackathon Teknium 🪽 Delphi Digital Tommy

Oyster Republic 🦪📲🦞👓

20,721 просмотров • 4 месяцев назад

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AI is leaving the data center. NVIDIA is betting on local, personal compute. Edge-agent research is showing why that matters: the closer the agent is to the user, the more usable it becomes. That is the OptimAI Claw thesis. Search brings the signal. Claw turns it into action on your device. The network keeps intelligence distributed. This is what agent-native infrastructure looks like when it is built for the edge.

OptimAI Network

26,868 просмотров • 2 месяцев назад

The Cost of Intelligence is Heading to Zero | Hyperspace P2P Distributed Cache We present to you our breakthrough cross-domain work across AI, distributed systems, cryptography, game theory to solve the primary structural inefficiency at the heart of AI infrastructure: most inference is redundant. Google has reported that only 15% of daily searches are truly novel. The rest are repeats or close variants. LLM inference inherits this same power-law distribution. Enterprise chatbots see 70-80% of queries fall into a handful of intent categories. System prompts are identical across 100% of requests within an application. The KV attention state for "You are a helpful assistant" has been computed billions of times, on millions of GPUs, identically. And yet every AI lab, every startup, every self-hosted deployment - computes and caches these results independently. There is no shared layer. No global memory. Every provider pays the full compute cost for every query, even when the answer already exists somewhere in the network. This is the problem Hyperspace solves where distributed cache operates at three levels, each catching a different class of redundancy: 1. Response cache Same prompt, same model, same parameters - instant cached response from any node in the network. SHA-256 hash lookup via DHT, with cryptographic cache proofs linking every response to its original inference execution. No trust required. Fetchers re-announce as providers, so popular responses replicate naturally across more nodes. 2. KV prefix cache Same system prompt tokens - skip the most expensive part of inference entirely. Prefill (computing Key-Value attention states) is deterministic: same model plus same tokens always produces identical KV state. The network caches these states using erasure coding and distributes them via the routing network. New questions that share a common prefix resume generation from cached state instead of recomputing from scratch. 3. Routing to cached nodes Instead of transferring KV state across the network for every request, Hyperspace routes the request to the node that already has the state loaded in VRAM. The request goes to the cache, not the cache to the request. Together, these three layers mean that 70-90% of inference requests at network scale never require full GPU computation. This work doesn't exist in isolation. It builds on research from across the industry: SGLang's RadixAttention demonstrated that automatic prefix sharing can yield up to 5x speedup on structured LLM workloads. Moonshot AI's Mooncake built an entire KV-cache-centric disaggregated architecture for production serving at Kimi. Anthropic, OpenAI, and Google all launched prompt caching products in 2024 - priced at 50-90% discounts - because system prompt reuse is so pervasive that it changes the economics of inference. What all of these systems share is a common limitation: they operate within a single organization's infrastructure. SGLang caches prefixes within one server. Mooncake disaggregates KV cache within one datacenter. Anthropic's prompt caching works within one API provider's fleet. None of them can share cached state across organizational boundaries. Hyperspace removes this boundary. The cache is global. A response computed by a node in Tokyo is immediately available to a node in Berlin. A KV prefix state generated for Qwen-32B on one machine is verifiable and reusable by any other machine running the same model. The routing network provides the delivery guarantees, the erasure coding provides the redundancy, and the cache proofs provide the trust. What this means for the cost of intelligence Big AI labs scale linearly: twice the users means twice the GPU spend. Every query is a cost center. Their internal caching helps, but it's siloed - Lab A's cache can't serve Lab B's users, and neither can serve a self-hosted Llama deployment. Hyperspace scales sub-linearly. Every new node that joins the network adds to the global cache. Every inference result enriches the cache for all future requests. The cache hit rate rises with network size because query distributions follow a power law - the most common questions are asked exponentially more often than rare ones. The implication is simple: as the network grows, the effective cost per inference drops. Not linearly. Logarithmically. At 10 million nodes, we estimate 75-90% of all inference requests can be served from cache, eliminating 400,000+ MWh of energy consumption per year and avoiding over 200,000 tons of CO2 emissions. The first person to ask a question pays the compute cost. Everyone after them gets the answer for free, with cryptographic proof that it's authentic. Training is competitive. Inference is shared Open-weight models are converging on quality with closed models. Labs will continue to differentiate on training - data curation, architecture innovation, RLHF tuning. That's where the real intellectual property lives. But inference is a commodity. Two copies of Qwen-32B running the same prompt produce the same KV state and the same response, byte for byte, regardless of whose GPU runs the matrix multiplication. There is no moat in multiplying matrices. The moat is in training the weights. A global distributed cache makes this separation explicit. It doesn't matter who trained the model. Once the weights are open, the inference cost approaches zero at scale - because the network remembers every answer and can prove it's correct. No lab, no matter how well-funded, can match this. They cannot share caches across competitors. They scale linearly. The network scales logarithmically. The marginal cost of intelligence approaches zero. That's the endgame.
0:23

The Cost of Intelligence is Heading to Zero | Hyperspace P2P Distributed Cache We present to you our breakthrough cross-domain work across AI, distributed systems, cryptography, game theory to solve the primary structural inefficiency at the heart of AI infrastructure: most inference is redundant. Google has reported that only 15% of daily searches are truly novel. The rest are repeats or close variants. LLM inference inherits this same power-law distribution. Enterprise chatbots see 70-80% of queries fall into a handful of intent categories. System prompts are identical across 100% of requests within an application. The KV attention state for "You are a helpful assistant" has been computed billions of times, on millions of GPUs, identically. And yet every AI lab, every startup, every self-hosted deployment - computes and caches these results independently. There is no shared layer. No global memory. Every provider pays the full compute cost for every query, even when the answer already exists somewhere in the network. This is the problem Hyperspace solves where distributed cache operates at three levels, each catching a different class of redundancy: 1. Response cache Same prompt, same model, same parameters - instant cached response from any node in the network. SHA-256 hash lookup via DHT, with cryptographic cache proofs linking every response to its original inference execution. No trust required. Fetchers re-announce as providers, so popular responses replicate naturally across more nodes. 2. KV prefix cache Same system prompt tokens - skip the most expensive part of inference entirely. Prefill (computing Key-Value attention states) is deterministic: same model plus same tokens always produces identical KV state. The network caches these states using erasure coding and distributes them via the routing network. New questions that share a common prefix resume generation from cached state instead of recomputing from scratch. 3. Routing to cached nodes Instead of transferring KV state across the network for every request, Hyperspace routes the request to the node that already has the state loaded in VRAM. The request goes to the cache, not the cache to the request. Together, these three layers mean that 70-90% of inference requests at network scale never require full GPU computation. This work doesn't exist in isolation. It builds on research from across the industry: SGLang's RadixAttention demonstrated that automatic prefix sharing can yield up to 5x speedup on structured LLM workloads. Moonshot AI's Mooncake built an entire KV-cache-centric disaggregated architecture for production serving at Kimi. Anthropic, OpenAI, and Google all launched prompt caching products in 2024 - priced at 50-90% discounts - because system prompt reuse is so pervasive that it changes the economics of inference. What all of these systems share is a common limitation: they operate within a single organization's infrastructure. SGLang caches prefixes within one server. Mooncake disaggregates KV cache within one datacenter. Anthropic's prompt caching works within one API provider's fleet. None of them can share cached state across organizational boundaries. Hyperspace removes this boundary. The cache is global. A response computed by a node in Tokyo is immediately available to a node in Berlin. A KV prefix state generated for Qwen-32B on one machine is verifiable and reusable by any other machine running the same model. The routing network provides the delivery guarantees, the erasure coding provides the redundancy, and the cache proofs provide the trust. What this means for the cost of intelligence Big AI labs scale linearly: twice the users means twice the GPU spend. Every query is a cost center. Their internal caching helps, but it's siloed - Lab A's cache can't serve Lab B's users, and neither can serve a self-hosted Llama deployment. Hyperspace scales sub-linearly. Every new node that joins the network adds to the global cache. Every inference result enriches the cache for all future requests. The cache hit rate rises with network size because query distributions follow a power law - the most common questions are asked exponentially more often than rare ones. The implication is simple: as the network grows, the effective cost per inference drops. Not linearly. Logarithmically. At 10 million nodes, we estimate 75-90% of all inference requests can be served from cache, eliminating 400,000+ MWh of energy consumption per year and avoiding over 200,000 tons of CO2 emissions. The first person to ask a question pays the compute cost. Everyone after them gets the answer for free, with cryptographic proof that it's authentic. Training is competitive. Inference is shared Open-weight models are converging on quality with closed models. Labs will continue to differentiate on training - data curation, architecture innovation, RLHF tuning. That's where the real intellectual property lives. But inference is a commodity. Two copies of Qwen-32B running the same prompt produce the same KV state and the same response, byte for byte, regardless of whose GPU runs the matrix multiplication. There is no moat in multiplying matrices. The moat is in training the weights. A global distributed cache makes this separation explicit. It doesn't matter who trained the model. Once the weights are open, the inference cost approaches zero at scale - because the network remembers every answer and can prove it's correct. No lab, no matter how well-funded, can match this. They cannot share caches across competitors. They scale linearly. The network scales logarithmically. The marginal cost of intelligence approaches zero. That's the endgame.

Varun

37,542 просмотров • 4 месяцев назад

The biggest platform upgrade since Mainnet, explained by our Co-Founder Alessandro De Carli ☁️ 🫡 Acurast is moving from a Node.js-only runtime to full Linux-based containers. Every Android Acurast Processor on the network now functions as a decentralized server with confidential execution at the hardware level. No language barriers, full Debian software ecosystem, SSH access, and a VPS-like experience. What you can build: autonomous AI agents, browser automation across thousands of devices, algorithmic trading bots, even game servers. Running across the Acurast network on Android smartphones in 175+ countries. Decentralized Cloud. Same developer experience. No compromises. The future of compute does not live in a data center, it lives in your pocket. 👇
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The biggest platform upgrade since Mainnet, explained by our Co-Founder Alessandro De Carli ☁️ 🫡 Acurast is moving from a Node.js-only runtime to full Linux-based containers. Every Android Acurast Processor on the network now functions as a decentralized server with confidential execution at the hardware level. No language barriers, full Debian software ecosystem, SSH access, and a VPS-like experience. What you can build: autonomous AI agents, browser automation across thousands of devices, algorithmic trading bots, even game servers. Running across the Acurast network on Android smartphones in 175+ countries. Decentralized Cloud. Same developer experience. No compromises. The future of compute does not live in a data center, it lives in your pocket. 👇

Acurast

41,729 просмотров • 3 месяцев назад

🔥🔥A NEW INFRASTRUCTURE LAYER FOR AI IN THE WEB3 ERA - 🟢#SaharasAI is the first full-stack AI-native platform on blockchain, offering a decentralized framework for creating, distributing, and monetizing AI models and datasets. The project is built around the principle of open access, privacy by design, and user-owned intelligence. 🔍 Key Differentiators: ✅ AI-Native Infrastructure: A modular stack covering compute, model, and data layers ✅ Cross-Chain Compatibility: Seamless deployment across multiple blockchain environments ✅ Monetization First: Built-in tools to tokenize, sell, and reward AI contributions ✅ ZK-Based Privacy: Zero-knowledge proofs for secure, verifiable computation and data usage 💼 Backed by Leading Institutions: YZi Labs, Microsoft, Amazon, Pantera Capital, Sequoia Capital... ⭐️ Community Sale on Buidlpad that WLFI refered about: ⏰ Date: June 8–11, 2025 💰 Price: $0.15/token 🎯 Allocation per user: $50 – $3,000 💳 Payment: USD1 or BNB ✅ KYC required 📎 Learn more: $SAHARA #SAHARA #buidlpad #AIforALL
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🔥🔥A NEW INFRASTRUCTURE LAYER FOR AI IN THE WEB3 ERA - 🟢#SaharasAI is the first full-stack AI-native platform on blockchain, offering a decentralized framework for creating, distributing, and monetizing AI models and datasets. The project is built around the principle of open access, privacy by design, and user-owned intelligence. 🔍 Key Differentiators: ✅ AI-Native Infrastructure: A modular stack covering compute, model, and data layers ✅ Cross-Chain Compatibility: Seamless deployment across multiple blockchain environments ✅ Monetization First: Built-in tools to tokenize, sell, and reward AI contributions ✅ ZK-Based Privacy: Zero-knowledge proofs for secure, verifiable computation and data usage 💼 Backed by Leading Institutions: YZi Labs, Microsoft, Amazon, Pantera Capital, Sequoia Capital... ⭐️ Community Sale on Buidlpad that WLFI refered about: ⏰ Date: June 8–11, 2025 💰 Price: $0.15/token 🎯 Allocation per user: $50 – $3,000 💳 Payment: USD1 or BNB ✅ KYC required 📎 Learn more: $SAHARA #SAHARA #buidlpad #AIforALL

BD GemX

22,815 просмотров • 1 год назад