Streaming Chat Inference

To stream LLM chat completions with fee settlement, you connect to 0G Compute providers, render token output in real time, and finalize payment by calling processResponse with the correct parameter order.

Settling 0G Compute inference requires extracting the chat ID from response headers first, falling back to the body if needed, and passing usage data to processResponse in the exact specified parameter order.

Yes, you can run non-streaming chat inference with DeepSeek, Qwen, Gemma, and GPT-OSS models on 0G Compute to send messages and receive standard assistant replies.

Yes, you need the ethers and @0glabs/0g-serving-broker dependencies to perform provider metadata lookup, authenticate request headers, and manage balance checks for resilient inference operations.

Chat inference fails without provider acknowledgement because the workflow requires capturing usage data and chat identifiers during the stream to correctly acknowledge and settle the request.

Build resilient multi-turn assistant sessions by handling retries, balance checks, and error recovery during real-time LLM output rendering to ensure continuous production chat app availability.