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Gate TradFi is a comprehensive trading section built by Gate. It brings together three core trading models: CFD contracts, perpetual contracts, and spot tokens. Through a unified entry point and a multi asset framework, users can allocate capital and switch strategies more flexibly across different markets.

Dolphin and Render are both DePIN projects that use distributed GPU resources to build infrastructure, but their core directions are not the same. Render is mainly focused on GPU rendering and digital content generation, while Dolphin is more centered on decentralized AI inference and AI infrastructure networks.

The core logic of Dolphin Network is to distribute AI model inference tasks across GPU nodes around the world for collaborative processing. Developers can call AI inference services through the network, while GPU owners can contribute idle computing power to execute tasks and earn DPHN token rewards. Through task scheduling, random validation, encryption, and economic incentives, Dolphin coordinates AI inference requests, GPU nodes, and result verification, forming a distributed AI inference infrastructure.

Dolphin (POD) is a Web3 AI infrastructure project built for decentralized AI inference and distributed GPU collaboration. Its core product, Dolphin Network, allows GPU owners around the world to share idle computing power and provide distributed inference services for AI models. Network participants can earn POD token rewards by processing inference requests, while developers gain access to AI capabilities in a more open and cost efficient way.

A CFD, or Contract for Difference, is a financial derivative settled based on the price difference of an underlying asset. Users do not need to actually hold the underlying asset itself. Instead, they can participate in the market by judging whether the price will rise or fall. For this reason, the core logic of CFDs is not the transfer of asset ownership, but trading price movements.

Sahara AI enables collaboration and value distribution across AI data, models, inference services, and AI Agents through on-chain ownership verification and off-chain AI execution. Its architecture combines blockchain transparency with AI computing capabilities to support open AI infrastructure. Sahara AI’s technical system includes modules such as a Layer1 blockchain, AI Marketplace, data service layer, AI Agent Economy, and Attribution System, with the goal of building decentralized infrastructure for AI collaboration and AI asset management.

Sahara AI (SAHARA) is an AI native blockchain platform built for artificial intelligence collaboration and asset ownership. Through blockchain mechanisms, it enables the sharing, licensing, and revenue distribution of AI data, models, computing power, and AI Agents. Its core goal is to build an open, transparent, and incentive driven decentralized AI economy.

Sahara AI Marketplace is the AI asset trading and collaboration platform within the Sahara AI ecosystem. It is used for uploading, licensing, calling, and distributing revenue from AI data, models, inference services, and AI Agents. Its core goal is to build an open and verifiable AI collaboration economy. Unlike traditional AI platforms, Sahara AI Marketplace records the sources of AI data and models through an on-chain Attribution mechanism, and automatically handles authorization and revenue settlement during AI service calls. This allows data contributors, model developers, and service providers to receive transparent revenue distribution.

Sahara AI and Bittensor are both decentralized AI infrastructure projects, but their core positioning is not the same. Sahara AI places greater emphasis on a collaborative system for AI data, models, Agents, and revenue distribution, while Bittensor focuses more on AI model inference networks and model competition mechanisms. Sahara AI uses an AI native Layer1 blockchain architecture, managing AI asset authorization, trading, and revenue distribution through Attribution and an AI Marketplace. Bittensor, by contrast, uses Subnets and incentive mechanisms to encourage model providers to produce high quality AI inference results.

Sei (SEI) is a Layer 1 blockchain that supports parallelized EVM execution. It is designed to improve the throughput and real-time interaction capabilities of on-chain applications through a low-latency consensus mechanism and high-performance execution architecture. As DeFi, blockchain games, and high-frequency trading applications continue to develop, Sei is widely used in scenarios that require fast confirmation and low-cost execution.

Parallelized EVM is an EVM architecture that allows non-conflicting transactions to execute simultaneously. Its goal is to break through the performance limits of the traditional sequential execution model while improving blockchain throughput and real-time interaction capabilities. As DeFi, blockchain games, and high-frequency on-chain applications continue to develop, parallel execution is becoming an important optimization direction for high-performance public blockchains.

Sei and Solana are both Layer 1 public blockchains designed for high throughput and low latency, but they differ clearly in their technical architecture and ecosystem strategy. Solana uses an independent runtime environment and a parallel execution structure, while Sei focuses on Parallelized EVM and Ethereum compatibility. Solana places greater emphasis on a native high-performance architecture, improving network throughput through the Sealevel parallel execution model and Proof of History. Sei, meanwhile, aims to improve on-chain execution efficiency through Parallelized EVM, Twin-Turbo Consensus, and optimized state management, while maintaining compatibility with Solidity and the EVM toolchain.

The Akash Network Provider mechanism is a decentralized computing supply system that allows individuals, mining farms, or data centers to provide GPU and server resources to the network and earn revenue by leasing computing resources. After developers submit GPU requirements, Providers participate in bidding, and the system then creates an on chain lease and deploys the workload.

The AI industry is evolving from a focus on training competition to the implementation of inference. While chips, HBM, and advanced packaging continue to be major bottlenecks, the fastest-growing areas are now inference infrastructure, data centers, power supply, cooling, and high-speed interconnects. Based on recent publicly available information, this article examines the most important AI infrastructure trends to follow in the next 2–3 years.

Akash Network’s GPU leasing process connects GPU providers with developers seeking hash power through an open marketplace. Once developers submit their computing requirements, network providers bid competitively. The system then creates a lease, deploys GPU resources, and settles payments in AKT.