You can use NEAR AI Cloud when you want to deploy AI models (open source or otherwise) in a fully private, verifiable way—your data, model weights, and outputs are protected with hardware enclaves and crypto verification. The platform supports high-volume inference, TEE-based security, model registry, and gateway APIs. Strong use cases include building autonomous agents, private LLM hosting, secure AI tools for sensitive data, or apps that require both scale and provable trust.
Integrations
Support for open models like Llama, DeepSeek, Qwen etc. via their model registry, Trusted Execution Environment hardware (Intel TDX, NVIDIA TEE) for secure inference, On-chain verification / blockchain smart contracts (NEAR protocol) for guarantees, LLM Gateway for routing and management of inference requests, Confidential GPU provider network with standardized 8× H200 GPU machines per node, Developer SDKs / APIs for private inference & verification
Use Cases
Hosting LLM-powered agents that need to act, reason, or transact securely
Building applications that process sensitive data (e.g. legal, health, finance) without exposing data or models
Deploying private chatbot or assistant services with strong privacy & verifiability
Running AI inference at scale with high throughput and low latency
Empowering organizations to use open source models confidently under strong security
Enabling decentralized AI ecosystems where compute is provided by GPU operators who are staked and verified
Standout Features
Verifiable private inference: every model execution is cryptographically signed and provable
Confidential GPU network via TEE hardware (Intel TDX + NVIDIA TEEs)
Unified registry of open AI models so you don't need to juggle multiple APIs
Scalable infrastructure: specialized GPU nodes (8× H200 GPUs) for high throughput
Blockchain / NEAR-protocol integration for guarantees and staking by GPU providers
Decentralized, privacy-first model training / fine-tuning + developer-friendly tools
Tasks it helps with
Deploy open models (Llama, DeepSeek, Qwen etc.) via a unified platform
Run inference in Trusted Execution Environments (TEEs) with GPU nodes
Ensure cryptographic attestation / verification of model execution
Use an LLM gateway to route requests securely
Enable decentralized, confidential model training/fine-tuning
