Mechanisms for collaborative working of technical components
In n8n's self-hosted AI suite, Ollama assumes the role of computational core as a runtime environment for large language models, supporting local operation of mainstream open source models such as Llama3, etc. Qdrant, as a high-performance vector database, realizes a processing capacity of 100,000+ queries per second through 128-dimensional vector indexes, and the two are seamlessly integrated through a REST API.
Performance Comparison Advantage
- Latency Optimization: Localized deployment reduces AI inference latency from 300-500ms for cloud-based services to 80-120ms
- cost-effectiveness: Running LLM locally reduces long-term cost of use by 70-90% compared to commercial AI APIs.
- Extension flexibilityQdrant's single-node throughput is up to 5000 QPS and supports horizontal scaling to millions of vector stores.
Practical application performance
In an intelligent chatbot scenario, the technology combination achieves an intent recognition accuracy of 98%. Document analysis workflow tests show that the average time to process 100 pages of PDF is 45 seconds, and the memory footprint is stable at less than 8GB.
This answer comes from the articlen8n Self-hosted AI Starter Kit: an open source template for quickly building a local AI environmentThe
