Prediction: By 2030, global data center workloads are projected... to split into: • 50% traditional workloads • 37% AI inference • 13% AI training Inference is set to become nearly 3× larger than training. As more AI applications move into production, builders will need infrastructure that can support lower-cost inference, reliable execution, faster routing and private workloads at scale. This is where FAR AI enters the market. Source: JLL Research, 2025show more

It’s live. AxonDAO’s GPU fleet is installed and will... soon be rentable on the open market via vast.ai. Premium GPU capacity - 8x NVIDIA B200 and 8x RTX Pro 6000 - built for large-scale AI training, inference, rendering, and research workloads. 🧵show more

ROBA Compute is our GPU execution layer for robotics.... A distributed infrastructure layer for simulation, training, inference, and autonomous workflows — built for our stack first, then opened to external builders. GPU rental is already a category. The missing layer is robotics-native infrastructure: templates, allocation, benchmarking, and persistent execution for embodied AI. That’s the opportunity. ROBA Compute is designed to become the infrastructure layer where robotics workloads actually run.show more

Every search, recommendation, simulation and AI-generated response depends on... rapid access to #data.​​ From training large AI models to enabling real-time inference, #DRAM helps keep today's most demanding workloads moving at the speed innovation requires.​​ This National DRAM day, join us in celebrating the technology that helps bridge the gap between processors and data, enabling everything from smartphones and PCs to #AI infrastructure.show more

Our newest AI accelerator Maia 200 is now online... in Azure. Designed for industry-leading inference efficiency, it delivers 30% better performance per dollar than current systems. And with 10+ PFLOPS FP4 throughput, ~5 PFLOPS FP8, and 216GB HBM3e with 7TB/s of memory bandwidth it's optimized for large-scale AI workloads. It joins our broader portfolio of CPUs, GPUs, and custom accelerators, giving customers more options to run advanced AI workloads faster and more cost-effectively on Azure.show more

70% of AI compute in 2026 is inference. Not... training. The models are built. The real infrastructure challenge is serving them at scale to millions of users and agents, every day, in production. Most enterprise infrastructure is still optimized for the wrong workload.🧵show more

China just took AI infrastructure underwater. The country has... reportedly launched a wind-powered underwater data center near Shanghai, designed to cool AI servers using seawater instead of traditional cooling systems 🌊🤖 Around 2,000 servers are said to be sealed inside submarine-grade modules, helping reduce land use, water consumption, and cooling energy. The project is powered by offshore wind and built for AI workloads, big data, and large-scale computing. Why it matters? AI is creating massive demand for electricity, cooling, and infrastructure. If underwater data centers prove reliable, they could become a new way to scale AI while using less energy. But the challenge is huge: corrosion, maintenance, sea conditions, and long-term reliability still need to be proven.show more

Today, Nesa is excited to announce cross-chain support with... Sei. As part of this new development, Nesa will be featuring a dedicated ramp for Sei’s rich ecosystem of applications to seamlessly integrate with AI. Now any Sei dapp can run AI inference on hundreds of models supported by Nesa, including the largest LLMs and most popular models across Vision, Language, and Generative AI. For developers, we’ll have information soon on how to access AI inference on Nesa via smart contracts from the Sei blockchain. Stay tuned for more details on this ground breaking development for decentralized AI.show more

Today, we're excited to announce a partnership with Manta... Network (🔱,🔱), the Modular L2 solution that is transforming the landscape of ZK. Nesa is bringing private AI inference to the Manta ecosystem through a specialized collaboration. For the first time, developers on Manta can access Nesa's full library of AI models on-chain, and enjoy lightning fast, end-to-end private AI inference without ever leaving the Manta ecosystem. This means that dapps and protocols can now fuse with AI via smart contract on Manta. This integration is set to redefine the future of decentralized technology with AI. Stay tuned for more updates on how Nesa and Manta Network will be shaping the future of crypto together.show more

🔥Nexera & Aethir: Unleashing AI’s Next Frontier Through Tokenized... GPU Power 🤝 Nexera is proud to join forces with Aethir in a strategic partnership to make cutting-edge AI infrastructure globally accessible. By tokenizing fractional GPU ownership, we’re enabling developers, enterprises, and investors everywhere to harness the explosive growth of deep learning and generative AI without being limited by geography, scale, or cost. With transparent tokenization, innovators can access powerful GPUs for faster model training and more advanced applications. GPU providers gain streamlined funding for expansion and upgrades, and investors tap into a high-growth market with secure, compliant opportunities that can provide higher yields than other RWA products. It’s an entirely new ecosystem where everyone can thrive, fueling AI’s evolution at an unprecedented pace. By 2030, the global GPU market is projected to exceed hundreds of billions of dollars, driven by the explosive demand for AI-powered applications, deep learning, and increasingly sophisticated generative models, ensuring that tokenizing these invaluable resources is poised to tap into a massive, rapidly expanding opportunity. $NXRAshow more

LocalHost (localhost HQ) is working with Anthropic (Anthropic) to... support founders building and deploying AI in production. Through this collaboration, LocalHost portfolio companies and a select group of high-signal community startups will receive Anthropic credits to build with Claude and experiment, iterate, and ship their applications. As more companies move from AI experimentation to production systems, access to reliable models and strong infrastructure matters. We’re excited to support founders who are designing products around AI from the start, rather than adding it on later.show more

Holy sh!t ! OpenAI will have their custom inference... chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.show more

$AMD CEO: “We are 2 years into 10-year AI... buildout.” Lisa Su said this last year and $AMD reached an all-time high this year as GPU and CPU demand is skyrocketing. We are still at just year 3 of a decade-long infrastructure buildout. McKinsey estimates AI infrastructure spending will reach $5-$8 trillion by 2030. And then we’ll still have at least a few more years of rapid buildout to reach sufficient capacity to train next-gen models and serve every inference demand at attractive prices. Then all this infrastructure will need to be renewed every 4-5 years. Selling $AMD here means selling it just at the beginning of the largest IT buildout.show more

"The best organizations won't manage token budgets manually. Instead,... they'll rely on orchestration layers that automatically route workloads to the right models based on performance, cost, and use case. Keeping track of which model is best for coding, finance, research, or support will become too complex for humans to manage directly, so intelligent routing will become a core part of enterprise AI infrastructure." Aravind Srinivas How will the best orgs of the future manage token budgeting Michael Mignano Nikesh Arora Ryan Petersen mmurphshow more

Most people think AI data centers are giant buildings... in the desert. One guy installed four mini Nvidia AI data centers right behind his work desk and now they pay him every month. Each unit is about the size of a small fridge. Inside: Nvidia GPUs running AI workloads 24/7. He hooked them up next to his AC system and that was basically it. Now the company pays him a flat monthly fee for the electricity and Wi-Fi they use. According to him, it brings in around $10,000/month straight into his account. The crazy part: the units also cool part of the house, cutting his AC bill by roughly $600. That’s more than $120,000 a year from four AI boxes sitting inside his home office. His mortgage is basically being paid by AI hardware behind his chair. Quietly, regular homes are starting to become AI infrastructure. Save this post. You’re watching the next gold rush move into people’s homes.show more

MERRY MAN // DAY 2 Yesterday we gave you... the first glimpse. Today, let’s clear up what we mean by privacy focused AI. A good reference point is what Venice AI has been building around VVV. Venice showed that using powerful AI doesn’t have to mean handing over a permanent record of everything you ask it. Their architecture includes local conversation history, zero retention inference, anonymized access to third party models and stronger privacy options through TEE and end to end encryption. Merry Man is our own take on where that idea can go inside Cerebro. Not just private chat, but a privacy focused agent that can actually work with the things you normally hesitate to give an AI. Your codebase. Private documents. Internal research. Personal data. Work that shouldn’t automatically become somebody else’s dataset. And there’s another part of the Venice/VVV model that caught our attention. VVV connects its token economy to access to private inference. Cerebro has been approaching the same broader problem from a different direction: holders are already earning inference credits, and Merry Man gives us somewhere native to start putting that inference to work. Private intelligence with an actual compute economy underneath it. That’s your Day 2 spoiler. Previews are coming soon.🧠show more

Ritual Community Builders Program Ritual is launching a global... builders network + research collective at the frontier of onchain AI. What it’s about: •Bringing together developers, researchers, and educators exploring AI, cryptography & blockchain as one discipline. •Covering cutting-edge topics: zkML, SMPC, verifiable inference, algorithmic ethics. •Builders don’t collect certificates, they run experiments, build proofs, and share code that moves the field forward. •Connecting academia, industry teams, and global dev communities to lower barriers for building AI-native protocols. Why it matters: • Turn theory into practice with paper-to-code workshops, demos, and technical deep dives. • Encourage ethical, privacy-preserving, and transparent AI development on-chain. • Make advanced AI knowledge accessible through bootcamps, study circles & multilingual content. • Unite Ritual’s ecosystem partners, research teams, and independent builders under one vision: open, provable AI. How to join: •Fill out the application form. •Applications reviewed in 1–2 weeks. •Selected builders join an onboarding call. •Receive toolkit, curriculum roadmap & access to bi-weekly sessions. •Start contributing: research, workshops, prototypes, or community activities. Apply now. 👉 Link in comments. Ritual Foundationshow more

NVIDIA might have just declared war on the cloud... GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a deskshow more

🚨 BREAKING: Starcloud just turned Starlink’s laser network into... the backbone for orbital AI data centers. A company called Starcloud has ordered 50+ Starlink Mini Laser terminals to equip 25+ future satellites. Not ground stations. Not fiber cables. Direct laser-linked computing nodes in orbit plugged straight into SpaceX’s space-based optical mesh. This is the sci-fi future arriving now: Orbital cloud computing AI servers floating in space Powered by 24/7 sunlight Connected globally at light speed via Starlink lasers The insane part: Starcloud says its satellites will eventually handle full AI inference and training workloads directly in orbit. Data won’t always need to come back to Earth to be processed. The advantages are massive: • Unlimited solar energy (no grid limits) • Zero land or water constraints • Passive radiative cooling in vacuum • Instant global relay with zero terrestrial bottlenecks • Near real-time Earth observation analysis Their first major spacecraft (Starcloud-3) is designed for 200 kilowatts in orbit a full-on space-based data center node, not just a satellite. And here’s the bigger picture: SpaceX has filed plans for up to ONE MILLION orbital data centers of its own. Read that again. We may be watching the birth of the first true space-based computing infrastructure layer for civilization. The internet already left the ground. Now AI might be next. What happens when the cloud literally moves into space? Follow for more frontier physics and future technology.show more

Every enterprise deploying AI is making a bet on... where their data infrastructure lives. Regulators, customers, and compliance teams now want clear answers about where data is stored and processed before they sign. Announced today at BoxWorks London: Box Zones expands to 10 regions worldwide: • 3 new Zones: Switzerland, Singapore, Israel • In-region compute for France & Canada • Roadmap: in-region AI processing later in 2026 According to Gartner, 35% of countries will be locked into region-specific AI platforms by 2027. Organizations that build their content infrastructure with residency at the center now, will be better positioned to win regulated customers and deploy AI responsibly. Read the full announcement:show more

🚀 Early Access to Sahara AI Studio is NOW... OPEN! The next phase of our testnet is here with exclusive early access to our all-in-one platform designed to transform the AI development lifecycle into a streamlined, integrated experience. Here’s everything you need to know 👇 AI development is fragmented. Devs juggle multiple tools, leading to inefficiencies & high costs. Sahara AI Studio integrates the entire AI lifecycle—from datasets & model training to secure storage & scalable compute—into one seamless experience: 📊 Data Hub: Discover, Manage, and Leverage AI-Ready Datasets Access high-quality, domain-specific, open-source and proprietary datasets through an integrated marketplace. Developers can download, import, or label datasets, making it easier to train and fine-tune models or deploy RAG pipelines. Secure uploads and seamless workflow integration enhance the experience. 🤖 Model Hub: Discover, Customize and Scale AI Workflows with Ease Discover ready-to-use open-source and proprietary models, RAG pipelines, and customizable workflows. Developers can deploy models quickly while maintaining privacy and security through Sahara Vaults. 🖥️ Compute Hub: Flexible, Scalable Compute Resources for AI Innovation Access scalable and secure computing resources tailored to diverse AI workloads. Trusted Execution Environment (TEE) capabilities ensure data privacy, while integration with top compute providers offer flexibility for developers. 🔐 Vaults: Secure Storage for AI Assets Securely store, organize, and manage datasets, models, and other assets in an encrypted central repository. Vaults offer scalability, reproducibility, and user control over AI resources. This is more than just beta testing a platform—it's your chance to help shape the future of decentralized AI development. 📅 How to Apply We're onboarding select developers in a phased approach. Early Access spots are limited, so apply now:show more