When accessing large language models hosted in China from outside the country, users often experience significant latency and instability. This is primarily due to network congestion and the cumulative time taken for establishing connections, including TCP and TLS handshakes, especially when new connections are made for each request. To address these issues, it is crucial to first measure performance metrics like time to first byte (TTFB) and time to first token (TTFT) from the actual user servers during peak hours. Utilizing streaming responses and ensuring the entire stream is read to completion can also help mitigate perceived latency and improve stability. AI
IMPACT Optimizing cross-border LLM access can improve user experience and application performance for global AI deployments.
RANK_REASON The item provides a technical guide and scripts for optimizing LLM performance, rather than announcing a new model or research breakthrough.
- Americas
- Beijing
- China
- curl
- Europe
- HTTPS
- .openai
- Python
- Southeast Asia
- Transmission Control Protocol
- Transport Layer Security
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