Key points for selecting large AI models for internal deployment in enterprises
A comprehensive guide to enterprise AI large-scale model deployment, covering selection points, technical details and best practices.
Core content:
1. Business demand analysis and data security considerations
2. Technology reserves and resource investment evaluation
3. Hardware configuration, software tool chain and model tuning strategy
The selection of solutions for deploying large AI models within an enterprise needs to comprehensively consider multiple dimensions such as business scenarios, data security, hardware performance, and operation and maintenance costs. Based on the search results, the following are the key selection points and recommended solutions:
1. Core factors for selection
1. Business needs and data sensitivity
- General scenarios (such as intelligent customer service, policy Q&A): give priority to mature NLP large models (such as the GPT series, Wenxin Yiyan), and adopt open source models or cloud APIs based on industry cases.
- High data sensitivity (such as finance, medical): must be privately deployed, and fine-tune the industry large model (L1/L2 level) based on enterprise data to avoid data outflow.
- Real-time requirements: choose a low-latency local inference solution, such as a training and push all-in-one machine.
2. Technical reserves and resource investment
- Enterprises with weak technical capabilities can choose pre-tuned full-stack all-in-one machines that integrate computing/storage/network/management tools to reduce deployment complexity.
- Enterprises with technical teams can build their own infrastructure, but they need to evaluate hardware procurement, network optimization and operation and maintenance costs.
2. Deployment mode selection
III. Infrastructure requirements
1. Hardware configuration
- Computing: NVIDIA A100/H100 GPU cluster is recommended, supporting 200G/400G RDMA network to meet multi-machine and multi-card training.
- Storage: High-performance shared storage (such as Ceph/distributed file system) is required, small file IO performance is optimized, and Checkpoint fast reading and writing is supported.
- Network: Low-latency InfiniBand or high-speed Ethernet to avoid training bottlenecks.
2. Software tool chain
- Training framework: PyTorch, TensorFlow, combined with DeepSpeed/Megatron-LM to optimize distributed training.
- Deployment tools: Ollama (local model running), OpenWebUI (graphical interaction), Docker/K8s containerization.
IV. Model tuning and data management
1. Model evaluation and iteration
- Use evaluation tools (such as SuperCLUE, Ragas) to compare the performance of the model in terms of understanding accuracy, result readability, and industry adaptability.
- Establish a feedback mechanism to optimize the L1/L2 model and support edge-cloud collaborative updates.
2. Data governance
- Data preprocessing: clean, label, and enhance industry data to build high-quality training sets.
- Storage optimization: Shared storage between training data and preprocessing platform to reduce copying time.
V. Security and O&M recommendations
1. Full-link security design
- Encrypt model transmission and storage, limit API access rights, and prevent model leakage.
- Audit data usage logs to meet compliance requirements such as GDPR.
2. O&M optimization
- Choose an all-in-one solution that supports unified monitoring and management to reduce the complexity of troubleshooting.
- Regular performance testing and hardware expansion to ensure 7x24-hour service stability.
Recommended solution
- General: Training and push all-in-one machine (such as Huawei Atlas 800) + industry fine-tuning NLP model (such as ChatGLM-6B), suitable for medium-sized enterprises.
- High-end customized: local GPU cluster (8*A100) + Kubernetes scheduling + private knowledge base, suitable for core scenarios such as finance/medical care.
