Knowledge Series | 6 Main DeepSeek Enterprise Deployment Models
IDC recently published an article titled “Behind the Explosion of DeepSeek: The Potential Impact of Large Model/Generative AI Market Ecosystem” that discusses:
“The deployment process for large models must meet the stringent requirements of high concurrency and low latency, while also considering factors like data security, privacy protection, resource scalability, and system maintenance. DeepSeek has introduced various deployment models, challenging the primary commercialization methods of global large model technology providers. Currently, the available deployment methods include cloud deployment, local/intranet deployment, edge deployment, hybrid deployment, containerized/microservices deployment, and federated deployment models.”
As seen, for enterprise users, DeepSeek large model deployments mainly have the six models listed above. So, what are the characteristics of each of these models, and what scenarios are they applicable for?
1、Cloud Deployment: DeepSeek large models deployed on public or private clouds, utilizing the infrastructure and resources of cloud providers. Applicable scenarios:
l Elastic demand: Requires dynamic resource adjustment based on load.
l Rapid scaling: Business growth is rapid, requiring quick system expansion.
l Cost optimization: Aims to reduce IT costs through a pay-as-you-go model.
2、Local/Intranet Deployment: DeepSeek large models deployed on enterprise internal servers or data centers, with data and applications running entirely within the enterprise’s intranet. Applicable scenarios:
l Data sensitivity: Requires high data security and full control over data.
l Compliance requirements: Needs to meet specific industry or regional compliance standards.
l Network limitations: The intranet environment cannot connect to external networks.
3、Edge Deployment: Deploying DeepSeek large models on edge nodes near data sources to reduce data transmission latency. Applicable scenarios:
l Low latency demands: For scenarios requiring fast responses, such as IoT or real-time monitoring.
l Limited bandwidth: When data transmission costs are high or bandwidth is limited, edge computing can reduce data upload.
l Offline operation: Requires continued operation even when the network is unstable or offline.
4、Hybrid Deployment: Combining cloud and local deployments, where some systems of DeepSeek large models are on the cloud, and others are on-premise. Applicable scenarios:
l Flexible demands: Some data needs local processing, while others require cloud processing.
l Transition phase: When migrating from local to cloud-based systems, hybrid deployment can serve as a transition.
l Disaster recovery: Backup between local and cloud systems enhances system reliability.
5、Containerized/Microservices Deployment: Breaking down the DeepSeek large model system into multiple microservices and using container technologies (like Docker) for deployment and management. Applicable scenarios:
l Agile development: Needs for rapid iteration and release of new features.
l Resource isolation: Different services require independent operating environments to avoid interference.
l Elastic scaling: Specific services can be independently scaled according to demand.
6、Federated Deployment: Multiple independent systems of DeepSeek large models collaborate via federated protocols to share data and resources, while maintaining independence. Applicable scenarios:
l Cross-organizational collaboration: Multiple organizations need to share data but maintain independent management.
l Data privacy: Requires data sharing while protecting data privacy.
l Distributed computing: Requires distributed data processing across multiple nodes, such as federated learning.
Thus, in general terms:
Cloud deployment achieves elastic scaling and cost optimization via cloud providers.
Local/intranet deployment ensures full control over data by utilizing on-premise data centers.
Edge deployment offers low latency and real-time processing through edge nodes.
Hybrid deployment combines local and cloud for flexibility and disaster recovery.
Containerized/microservices deployment facilitates agile development and resource isolation through container technologies and microservice architecture.
Federated deployment enables cross-organizational collaboration and data privacy protection via federated protocols and distributed architectures.
Enterprise users can select the appropriate deployment model based on their specific needs to optimize system performance and costs.
On February 2nd, ZStack announced that its AI Infra platform, AIOS fully supports private deployment of DeepSeek V3, R1, and JanusPro models. It is compatible with various CPUs/GPUs such as those from NVIDIA and Intel, reducing the barrier for enterprise users to privately deploy and apply DeepSeek.
As a DeepSeek On-Premises Deployment Expert, ZStack AIOS not only fully supports the above six DeepSeek enterprise deployment modes, but in the fifth mode, it can support containerized/microservices deployment as well as virtual machine and bare metal deployment.
As a DeepSeek enterprise expert, ZStack AIOS not only fully supports the six DeepSeek enterprise deployment models mentioned above but also supports containerized/microservices deployment and virtual machine, bare-metal deployments under the fifth model.
As the next-generation AI Infra platform, ZStack AIOS has been included in the report for its all-in-one platform advantages in computing resource scheduling, training and inference for various large models like DeepSeek, and AI application service development. It can help enterprise users improve heterogeneous hardware utilization, reduce AI costs, accelerate multi-model collaboration, optimize AI performance, and enable full-scale metering and billing to achieve AI self-service, thus accelerating AI privatization for enterprise applications.
