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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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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.
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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. 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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

37,362 Aufrufe • vor 4 Monaten

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. 👇
5:07

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

38,689 Aufrufe • vor 2 Monaten

🔥🔥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 Aufrufe • vor 1 Jahr

$AMD is providing receipts right now winning inference workloads from one of the best implementers of AI on the planet in $META And don’t @ me about the warrants. No one will care when AMD market cap goes above a trillion. 👏🏻🔥🚀
1:22

$AMD is providing receipts right now winning inference workloads from one of the best implementers of AI on the planet in $META And don’t @ me about the warrants. No one will care when AMD market cap goes above a trillion. 👏🏻🔥🚀

Daniel Newman

21,024 Aufrufe • vor 4 Monaten