中芸汇科技
Private Deployment Solution for Large Models in Data-Sensitive Industries

Private Deployment Solution for Large Models in Data-Sensitive Industries

On-premises deployment of open-source large models, keeping data within the internal network. Supports model quantization, lightweight compression, compute optimization for cost reduction, Docker/K8s container packaging, inference cluster setup, and hybrid cloud architecture. Long-term usage cost is only 1/5 to 1/3 of cloud APIs.

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LLM Private Deployment Service
LLM Private Deployment Service

Why Private Deployment Is a Must for Data-Sensitive Industries

Industries with strict data security requirements such as finance, healthcare, and government cannot send data to public large models. Private deployment keeps data within the internal network, while long-term usage costs are only 1/5 to 1/3 of cloud APIs. According to IDC, China's intelligent computing power reached 725.3 EFLOPS in 2024, a year-on-year increase of 74.1%. Rapid expansion of computing supply continues to lower the threshold for private deployment.

> Gartner's "Hype Cycle for Artificial Intelligence, 2025" points out: over 57% of enterprise data has not yet reached AI readiness. Private deployment is not only a requirement for security and compliance, but also the infrastructure for enterprises to accumulate AI capabilities and build a data flywheel.

Solution Overview: Core Advantages of Private Deployment

  • Zero Data Leakage: All inference requests are processed within the internal network; logs remain on the server, meeting Classified Protection 2.0/3.0 requirements
  • Model Quantization & Lightweight Compression: After INT4/INT8 quantization, model size is reduced by 60%-75%, and inference speed increases by 2-3 times
  • Compute Optimization for Cost Reduction: vLLM inference acceleration + KV Cache optimization boosts single-card throughput by 3-5 times
  • Docker/K8s Container Packaging: One-click deployment, elastic scaling, consistent environments from development to production
  • Hybrid Cloud Architecture: Core data stays on-premises, general capabilities in the cloud, balancing security and cost

    • Technical Architecture: Deployment Solutions for Enterprises of Different Sizes

    Lightweight Solution: 7B-Parameter Model on a Single Card

    Suitable for SMEs or single business scenarios. After INT4 quantization, a 7B model requires only 6-8GB of VRAM and can run on a single A10/A100, with inference speed of 50-80 tokens/s. Initial hardware investment: RMB 100,000-200,000 (including server), with average monthly O&M cost under RMB 5,000.

    Standard Solution: 14B-34B Parameter Model on Dual/Quad Cards

    Suitable for multi-scenario applications in mid-sized enterprises. 2-4 A100 GPUs provide enterprise-grade inference, supporting over 100 concurrent users. Typical deployment cost: RMB 400,000-800,000, with payback in 6-12 months through saved API call fees.

    Enterprise Solution: 70B+ Parameter Model with Inference Cluster

    Suitable for core business in large enterprises. A cluster of 4-8 A100/H800 GPUs supports high-concurrency, low-latency scenarios. Combined with a hybrid cloud architecture, core data is processed locally while general scenarios leverage cloud-based large models, achieving optimal overall cost.

    Quantified Benefits

    Solution LevelHardware InvestmentInference PerformanceApplicable Scenarios
    LightweightRMB 100-200k50-80 tokens/sSingle business scenario
    StandardRMB 400-800k100+ concurrent usersMulti-scenario applications
    EnterpriseRMB 1M+High concurrency, low latencyCore business + hybrid cloud

    Long-term usage cost is only 1/5 to 1/3 of cloud APIs, with payback in 6-12 months through saved API call fees.

    Scope of Applicability

    Suitable for: Data-sensitive industries like finance, healthcare, and government; enterprises with annual API call fees exceeding RMB 200,000; organizations with strict data compliance requirements (Classified Protection 2.0/3.0).

    Not suitable for: Teams with low data compliance requirements and low AI usage frequency (less than 10,000 calls per month) — direct use of cloud APIs is more economical.

    Frequently Asked Questions

    How much GPU computing power is needed for private deployment of large models?

    A 7B-parameter model (e.g., Qwen2-7B) can be deployed with a single A10/A100 card. For a 70B-parameter model, a 4×A100 cluster is recommended. According to IDC, China's intelligent computing power reached 725.3 EFLOPS in 2024, a year-over-year increase of 74.1%. Rapid expansion in computing supply is driving annual deployment cost reductions of over 30%. Enterprises can scale on demand based on business volume, with initial investment as low as under RMB 100,000.

    Can the performance of privately deployed large models approach that of public cloud APIs?

    Yes. Through INT4/INT8 quantization and vLLM inference acceleration, a 7B model on a single card can achieve 50-80 tokens/s, approaching or even exceeding some public cloud API response speeds. The core advantages are zero data leakage, lower latency (direct internal network connection <10ms), and no API call fees. Long-term usage cost is only 1/5 to 1/3 of cloud APIs.

    How to upgrade and maintain the model after private deployment?

    We provide a complete model version management and canary release mechanism: new models are first validated in a low-traffic environment, and traffic is gradually increased once stability is confirmed. We also offer a continuous monitoring dashboard that tracks inference latency, throughput, and anomaly rate in real time, with automatic rollback to the previous stable version if performance degradation occurs.