Paper Proposes Private DeepSeek Deployment Architecture

Song Tang’s paper proposes an AI-oriented architecture for private deployment of DeepSeek in computer AI systems, aiming to narrow the gap between large language model capabilities and local engineering deployment. The design combines quantization-aware model loading, heterogeneous GPU memory allocation, KV-cache reuse, retrieval-augmented generation, semantic reranking, request-aware inference scheduling, and policy-based security routing.

The system links model inference, vector retrieval, cache updates, access control, and fault isolation in one service graph. Tests used mixed question-answering, domain-document retrieval, and multi-turn reasoning requests. As concurrent requests rose from 20 to 100, average response latency increased from 0.82 seconds to 1.94 seconds; it stayed below 1.30 seconds at 60 concurrent requests.

With the complete deployment scheme, throughput reached 79 requests per second, 88.1% higher than single-node deployment. The paper says the method supports secure, efficient deployment that adapts to domain knowledge while preserving local data boundaries and traceable reasoning control during continuous operation.

Song Tang’s paper proposes an AI-oriented architecture for private deployment of DeepSeek in computer AI systems, aiming to narrow the gap between large language model capabilities and local engineering deployment. The design combines quantization-aware model loading, heterogeneous GPU memory allocation, KV-cache reuse, retrieval-augmented generation, semantic reranking, request-aware inference scheduling, and policy-based security routing.

The system links model inference, vector retrieval, cache updates, access control, and fault isolation in one service graph. Tests used mixed question-answering, domain-document retrieval, and multi-turn reasoning requests. As concurrent requests rose from 20 to 100, average response latency increased from 0.82 seconds to 1.94 seconds; it stayed below 1.30 seconds at 60 concurrent requests.

With the complete deployment scheme, throughput reached 79 requests per second, 88.1% higher than single-node deployment. The paper says the method supports secure, efficient deployment that adapts to domain knowledge while preserving local data boundaries and traceable reasoning control during continuous operation.