Unverified50% confidenceOpinionExact time
A great deal of local LLM knowledge can be learned through APIs, and learning can also be done with small models that fit within available VRAM or CPU resources.
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe plugin uses local inference, with models running directly on the user's own hardware (CPU/GPU) and all data processing completed on the local machine.70% similarUnverifiedThe plugin mechanism allows large language models to obtain real-time data and perform specific operations by calling external APIs, addressing the limitations of LLMs' knowledge cutoffs and inability to interact with external systems.69% similarUnverifiedPrompt Engineering, tokens, context windows, and API calls are the foundational prerequisite knowledge for interacting with large models.67% similarUnverified桌面端TTS将模型下载到本地执行推理，利用用户自有CPU/GPU算力，部署完成后无需持续服务器开支，这是Vois能提供无限生成的技术基础65% similarUnverifiedEmbedding API documentation directly into prompts leverages the In-Context Learning capability of large language models, requiring no updates to model weights.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/905728API
curl https://kongchang.com/api/v1/knowledge/claims/905728MCP
get_claim(id=905728)