How to Choose a Server for Self-hosted AI | AIOOS

Start with the workload boundary

An AI application server may only run a web interface, database and workflow engine while calling a hosted model API. That is very different from loading a model locally. Write down which components run on the machine before comparing plans.

CPU and memory

Application stacks such as Dify include multiple services, so memory headroom matters. Local inference depends on the exact model, quantization and context size. Do not buy from a generic label such as AI-ready without checking the actual workload.

Disk and network

Model files, container images, vector indexes, uploads and backups all consume disk. Network needs depend on whether users upload files, stream responses or download models. Check both bandwidth and traffic accounting.

A safer buying process

• Begin with a test environment and measure it.

• Prefer monthly billing until the route and workload are verified.

• Keep the database and uploaded files backed up outside the instance.

• Scale from observed memory, latency and disk metrics.

• Treat CPU-only and GPU inference as separate product decisions.

Continue

Compare the AIOOS server plans, then follow the Dify deployment guide or Ollama installation guide.

先明确工作负载边界

AI 应用服务器可能只运行 Web 界面、数据库和工作流引擎，并通过 API 调用托管模型；这与在服务器本地加载模型完全不同。比较配置前，先写清楚哪些组件实际运行在这台机器上。

CPU 与内存

Dify 等应用栈包含多个服务，因此需要留出内存余量。本地推理资源则取决于具体模型、量化方式和上下文长度。不要仅凭“AI 服务器”标签购买，应核对真实工作负载。

硬盘与网络

模型文件、容器镜像、向量索引、上传文件和备份都会占用硬盘。网络需求取决于用户是否上传文件、流式接收结果或下载模型，同时要确认带宽和流量计费方式。

更稳妥的购买流程

• 从测试环境开始，并记录真实指标。

• 在线路和负载验证前优先选择月付。

• 将数据库和上传文件备份到实例之外。

• 根据实际内存、延迟和硬盘数据再扩容。

• 将 CPU 推理与 GPU 推理视为不同的产品决策。

继续阅读

比较 AIOOS 服务器方案，再阅读 Dify 部署教程或 Ollama 安装教程。