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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.

NEAR Protocol

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73,204 次观看 • 1 个月前 •via X (Twitter)

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George Zeng explains NEAR’s approach to user-owned AI. Confidential inference keeps your data yours. IronClaw runs agents where secrets never reach the model. NEAR Intents settles across chains. Intelligence, execution, settlement. The full stack, and it answers to you.

George Zeng explains NEAR’s approach to user-owned AI. Confidential inference keeps your data yours. IronClaw runs agents where secrets never reach the model. NEAR Intents settles across chains. Intelligence, execution, settlement. The full stack, and it answers to you.

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AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:

AI inference is already live on UOMI Router. Access frontier open-source models through a decentralized inference network with: • Low-cost inference • OpenAI-compatible APIs • Verifiable compute • On-chain settlement Start building: And if you have idle GPUs, there's now a second way to participate. The UOMI Provider whitelist is officially open. Instead of letting your hardware sit unused, you can contribute GPU power to the UOMI Inference Network and earn from real AI demand. Join the Provider whitelist:

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OptimAI Core Node - More Than Mining. It’s Thinking. What does the OptimAI Core Node really do? + Crawls and indexes real-time web data + Runs lightweight AI inference tasks + Stores and retrieves data collaboratively + Powers decentralized search and compute Each node you run becomes part of a living AI infrastructure - one that learns, evolves, and grows smarter with every participant. The future of intelligence isn’t centralized. It’s distributed, and it starts with us. 👉 Join the network: 🎥 Watch the installation guide from our CCO, Ricardo, now for Windows users.

OptimAI Core Node - More Than Mining. It’s Thinking. What does the OptimAI Core Node really do? + Crawls and indexes real-time web data + Runs lightweight AI inference tasks + Stores and retrieves data collaboratively + Powers decentralized search and compute Each node you run becomes part of a living AI infrastructure - one that learns, evolves, and grows smarter with every participant. The future of intelligence isn’t centralized. It’s distributed, and it starts with us. 👉 Join the network: 🎥 Watch the installation guide from our CCO, Ricardo, now for Windows users.

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Lightchain AI

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Destra Network

20,224 次观看 • 7 个月前

A glimpse into one of our core features 👀 Chat, Search, Compare, & Run #Inference for all AI models on ONE Platform: No more complicated setups, just seamless #AI magic. 💫 Be the first to experience - join waitlist now: #Inferium #AI #Inference

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Inferium

1,079,311 次观看 • 2 年前

🎤 Pavel Durov, founder and CEO of Telegram Messenger, spoke at Blockchain Life 2026 2025 about Telegram Gifts, MiniApps success, and the dangers of centralized AI. Pavel announced Cocoon (Confidential Compute Open Network), a privacy-focused decentralized AI network powered by TON, launching in November. What it offers: 🔹 GPU owners can mine $TON by contributing computing power 🔹 App developers get secure inference without centralized data centers 🔹 Users experience AI services with complete privacy Apply: Watch the full speech 👇

🎤 Pavel Durov, founder and CEO of Telegram Messenger, spoke at Blockchain Life 2026 2025 about Telegram Gifts, MiniApps success, and the dangers of centralized AI. Pavel announced Cocoon (Confidential Compute Open Network), a privacy-focused decentralized AI network powered by TON, launching in November. What it offers: 🔹 GPU owners can mine $TON by contributing computing power 🔹 App developers get secure inference without centralized data centers 🔹 Users experience AI services with complete privacy Apply: Watch the full speech 👇

TON 💎

53,728 次观看 • 9 个月前

One account. No borders. Your financial life, on-chain. Trade, access yield, and go confidential across 35+ networks from a single account, powered by NEAR. Get started:

One account. No borders. Your financial life, on-chain. Trade, access yield, and go confidential across 35+ networks from a single account, powered by NEAR. Get started:

NEARWEEK

16,449 次观看 • 4 个月前

Distributed compute needs to be deployed with speed, scale, and sovereignty. Dan Wright joined Bloomberg Technology this week to talk about Galleon Forge One, our Series B, and the infrastructure buildout required to meet growing demand for inference, fine-tuning, and distributed model deployment at scale. The next wave of compute will not live only in centralized clouds. It will need to be deployed where land, energy, and data already exist. That is what Armada was built for. That is what Galleon delivers.

Distributed compute needs to be deployed with speed, scale, and sovereignty. Dan Wright joined Bloomberg Technology this week to talk about Galleon Forge One, our Series B, and the infrastructure buildout required to meet growing demand for inference, fine-tuning, and distributed model deployment at scale. The next wave of compute will not live only in centralized clouds. It will need to be deployed where land, energy, and data already exist. That is what Armada was built for. That is what Galleon delivers.

Armada

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Hyra AI Reborn. Stronger. Faster. Decentralized. 🔥 Reimagined. A new direction. A new era of decentralized intelligence. From privacy-first edge compute → to a global AI mesh owned by the people. From inference on your device → to digital sovereignty at scale. ✨ Every device is now a node. ✨ Every user is a Hyrasapien. ✨ Every action builds the future of AI. We’re not just rebranding ~ we’re reshaping the narrative of AI. Together, let’s build an AI infrastructure that’s secure, verifiable, and community-owned. 🔑 Your Device. Your Power. Your Sovereignty. 🌐 Explore the new era:

Hyra AI

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Bankless

113,516 次观看 • 9 天前

$INTERVIEW: AI, Surveillance, & the Fight for Digital Sovereignty | Near's Illia Polosukhin What happens when powerful AI requires an ID, governments can restrict access by country, and your most private data flows through centralized systems? NEAR Protocol co-founder Illia (root.near) (🇺🇦, ⋈) joins David Hoffman to explain why AI export controls could fracture the internet, how nationalized AI could become a surveillance layer, and why decentralized, user-owned AI may be the only real alternative. They also unpack Ironclaw, confidential inference, private agents, and NEAR’s push to make AI and assets fully sovereign. [TIMESTAMPS] 0:00 Intro 3:47 Pandora’s Box Opens 6:03 AI Meets Government 7:16 Decentralizing the Stack 8:51 Why Ban Fable? 11:18 Internet Balkanization 13:57 KYC for AI? 18:43 Nationalized Models 21:07 Building Private AI 26:30 Agent Marketplace 30:26 User-Owned Alignment 35:28 Winning Against Giants 40:19 Regulation Fuels Crypto 42:24 Global AI Adoption 45:05 Compute Is the Bottleneck 49:00 Enterprise Use Cases 51:39 Confidential TVL Explained 55:25 Full Sovereign Mode$

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Bankless

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Perplexity CEO Aravind Srinivas on the biggest threat to the data center industry: It's not competition. It's not regulation. It's decentralisation. "The biggest threat to a data center is if the intelligence can be packed locally on a chip that's running on the device and then there's no need to inference all of it on like one centralized data center." He outlines how this could work in practice. Personalisation doesn't necessarily require on-device model training. Retrieval augmented generation, tool calls, and local data can already tailor AI to individual users. But the real unlock? Test time training. Aravind Srinivas describes a future where AI lives on your device, watches how you work and gradually automates your repetitive tasks. "Imagine we crack test time training where the AI watches tasks you repeatedly do on your local system, adapts to you over time and starts automating a lot of the things you do." The key insight: in this model, the intelligence belongs to you. It's your data, your device, your personalised AI brain. And if that future arrives, the economics of centralised infrastructure start to collapse. "That really disrupts the whole data center industry. It doesn't make sense to spend all this money, 500 billion, 5 trillion, whatever on building all the centralized data centers across the world that do a lot of the intelligence workloads for people." The companies spending trillions on centralised infrastructure may want to rethink where intelligence actually needs to live.

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MUST WATCH: Governor Josh Shapiro calls out an AI data center developer in Montgomery County: "One developer is trying to bully his way into forcing data center projects through local opposition from the community and opposition from local elected officials... I'm a hell no on that project. I'm a hell no on many other projects across Pennsylvania."

Pennsylvania Governor's Office

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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. 👉

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. 👉

OptimAI Network

29,896 次观看 • 6 个月前

NEW: Premium Inference 101 The Economics & Infrastructure Behind Running Trillion Parameter Models Rodrigo Liang, CEO & Co-Founder of SambaNova "Inference has arrived. 70-80% of those racks are running inference." "[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable." "With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks." SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW. They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis. The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens. Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic. We Cover: › Why inference will need orders of magnitude more chips than training ever did › The 10kW rack vs the 130kW rack, & why air cooling decides geography › Running a 1T parameter model in one rack at full precision, no quantization › The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone › Revenue per rack, & why inference providers have revenue but no margin › JPMorgan, sovereignty, & the move back to on-prem Filmed at the RAISE Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems (00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation (03:00) The Inference problem nobody saw coming (04:52) SambaNova's chip evolution (07:19) Running a trillion-parameter model on a single rack (11:00) Do $100 billion data centers actually make sense? (14:14) What "premium inference" really means (18:28) Speed is about to become AI's biggest price tag (20:43) Starlink, edge computing, & AI reaching every corner of the planet (24:27) Working alongside NVIDIA & rival chipmakers (27:49) How customers actually measure inference performance (32:07) The biggest bottlenecks in AI's global land grab (35:12) Justifying the billion-dollar AI valuations (37:53) Why SambaNova refuses to build its own cloud (41:03) The "AI sovereignty" debate (43:48) Data privacy fears are driving the return to on-prem AI (47:55) How to actually get ROI out of AI spend (51:16) The one question every business should be asking about AI (56:09) The mentors & lessons behind a 32-year career in chips (58:02) Unveiling SambaNova's newest chip, the SN50

NEW: Premium Inference 101 The Economics & Infrastructure Behind Running Trillion Parameter Models Rodrigo Liang, CEO & Co-Founder of SambaNova "Inference has arrived. 70-80% of those racks are running inference." "[Inference services] are generating lots of revenue, but not enough margin. In order for them to sustain, they've gotta be more profitable." "With SambaNova, that min quantum is down to 1 rack. Where if you have other service providers, [with] say, a DeepSeek model, now 1.5 trillion parameters, to run that, the min for some of the other providers might be 10-20 racks." SambaNova builds full-stack inference infrastructure. 16 chips to a 10kW air-cooled rack that runs trillion parameter models, where a GPU rack pulls 130kW. They just demonstrated the fastest MiniMax M2.7 inference in the world, as benchmarked by Artificial Analysis. The demo paired one NVIDIA H200 rack for prefill with one SambaRack SN50 for decode. Disaggregated inference: GPUs load the context, RDUs generate the tokens. Now serving JPMorgan, SoftBank, Saudi Aramco & DOE national labs, just valued at $11B on a $1B Series F led by General Atlantic. We Cover: › Why inference will need orders of magnitude more chips than training ever did › The 10kW rack vs the 130kW rack, & why air cooling decides geography › Running a 1T parameter model in one rack at full precision, no quantization › The agent latency problem: 20 agents, 2 seconds each, 40 seconds gone › Revenue per rack, & why inference providers have revenue but no margin › JPMorgan, sovereignty, & the move back to on-prem Filmed at the RAISE Summit in Paris. Thank you to Brex, MongoDB & AssemblyAI for helping make this trip & content series happen. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Rodrigo Liang , Co-Founder & CEO at SambaNova Systems (00:59) SambaNova’s Series F: $1B raise at an $11 billion valuation (03:00) The Inference problem nobody saw coming (04:52) SambaNova's chip evolution (07:19) Running a trillion-parameter model on a single rack (11:00) Do $100 billion data centers actually make sense? (14:14) What "premium inference" really means (18:28) Speed is about to become AI's biggest price tag (20:43) Starlink, edge computing, & AI reaching every corner of the planet (24:27) Working alongside NVIDIA & rival chipmakers (27:49) How customers actually measure inference performance (32:07) The biggest bottlenecks in AI's global land grab (35:12) Justifying the billion-dollar AI valuations (37:53) Why SambaNova refuses to build its own cloud (41:03) The "AI sovereignty" debate (43:48) Data privacy fears are driving the return to on-prem AI (47:55) How to actually get ROI out of AI spend (51:16) The one question every business should be asking about AI (56:09) The mentors & lessons behind a 32-year career in chips (58:02) Unveiling SambaNova's newest chip, the SN50

Molly O’Shea

134,960 次观看 • 1 个月前

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.

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.

Cristiano R. Amon

15,783 次观看 • 1 个月前

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:

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

19,365 次观看 • 8 个月前

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

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 次观看 • 5 个月前

$🚨 I DON'T KNOW WHAT PEOPLE ARE WAITING FOR! This guy just deployed a full GPU compute farm in a data center Not renting cloud instances, not paying per-token, just racks of consumer GPUs on warehouse shelving doing AI inference 24/7 The setup is wild: open-frame rigs with rows of white fans, PCIe riser cables, server-grade PSUs, and a PDU for power distribution. All mounted on red industrial shelving with raised-floor cooling underneath. It looks like a crypto mining op, because it probably was one Here's what's actually happening: > Ex-mining rigs are being redeployed for AI inference at scale > Each rig runs 6-8 GPUs on PCIe risers connected to a single motherboard > Fans pull cold air from the raised floor through the open frames > A PDU on each rack handles power distribution across all rigs > The whole thing runs in a colocation facility with cheap power rates The economics make sense: cloud GPU pricing runs $2-4/hr per card. Own the hardware, colocate it for $0.08-0.12/kWh, and you're printing compute at a fraction of the cost The crypto-to-AI pipeline is real. Same hardware, same facilities, same power contracts, completely different revenue model Bookmark this!$

🚨 I DON'T KNOW WHAT PEOPLE ARE WAITING FOR! This guy just deployed a full GPU compute farm in a data center Not renting cloud instances, not paying per-token, just racks of consumer GPUs on warehouse shelving doing AI inference 24/7 The setup is wild: open-frame rigs with rows of white fans, PCIe riser cables, server-grade PSUs, and a PDU for power distribution. All mounted on red industrial shelving with raised-floor cooling underneath. It looks like a crypto mining op, because it probably was one Here's what's actually happening: > Ex-mining rigs are being redeployed for AI inference at scale > Each rig runs 6-8 GPUs on PCIe risers connected to a single motherboard > Fans pull cold air from the raised floor through the open frames > A PDU on each rack handles power distribution across all rigs > The whole thing runs in a colocation facility with cheap power rates The economics make sense: cloud GPU pricing runs $2-4/hr per card. Own the hardware, colocate it for $0.08-0.12/kWh, and you're printing compute at a fraction of the cost The crypto-to-AI pipeline is real. Same hardware, same facilities, same power contracts, completely different revenue model Bookmark this!

cristal

16,305 次观看 • 1 个月前