What Hardware Do You Need to Run AI Locally

Why You Do Not Need a GPU

The most common misconception about self-hosted AI is that you need expensive GPU hardware. GPUs are required for training large language models from scratch, which costs millions of dollars and is done by companies like Anthropic, OpenAI, and Google. You are not training models. You are running an AI platform that uses those models through API calls. The actual model inference happens on the cloud provider's GPU servers. Your server handles everything else: storing data, managing processes, running local ML models for classification, generating embeddings, and orchestrating agent workflows.

Local ML tasks like embedding generation and classification models run efficiently on modern CPUs. Libraries like sentence-transformers generate vector embeddings on CPU without noticeable delay for typical business workloads. You only need a GPU if you plan to run large language models locally, which is a different and much more expensive proposition than the hybrid approach most self-hosted AI deployments use.

Minimum Hardware Requirements

For a small deployment running a few AI agents with moderate knowledge bases, the minimum requirements are modest:

• CPU: 4 cores, modern x86_64 processor (Intel Xeon, AMD EPYC, or equivalent desktop processors)
• RAM: 16 GB minimum. This handles the AI platform, local ML models, and database operations comfortably for small workloads.
• Storage: 100 GB SSD. Knowledge bases, embeddings, logs, and operational data accumulate over time. SSD is important for the database and embedding retrieval performance.
• Network: Stable internet connection for cloud AI model API calls. Bandwidth requirements are modest since you are sending text prompts and receiving text responses.
• OS: Linux (Amazon Linux, Ubuntu, Debian, CentOS, or similar). The AI platform and its dependencies are built for Linux environments.

Recommended Hardware for Production

For production deployments handling real business workloads with multiple agents, larger knowledge bases, and continuous operation:

• CPU: 8 or more cores. More cores allow more concurrent agent operations and faster local ML processing.
• RAM: 32 GB. Provides comfortable headroom for larger knowledge bases, more concurrent operations, and database caching.
• Storage: 500 GB to 1 TB SSD. Gives room for growing knowledge bases, extensive audit logs, and backup storage. NVMe SSDs provide the best performance for database operations.
• Network: Stable broadband with low latency to major cloud providers. AI model API calls are latency-sensitive, so reliable connectivity matters.

Cloud Instance Sizing

If you are deploying on a cloud instance, here are equivalent sizing recommendations. On AWS, an m5.xlarge (4 vCPU, 16 GB RAM) handles small deployments and an m5.2xlarge (8 vCPU, 32 GB RAM) handles production workloads. On DigitalOcean, equivalent droplets in the General Purpose category work well. On Google Cloud, n2-standard-4 and n2-standard-8 instances provide comparable performance. Attach an SSD block storage volume for data that scales independently of the instance.

Scaling Considerations

As your AI deployment grows, you have two scaling paths. Vertical scaling means upgrading to a bigger server with more CPU, RAM, and storage. This is the simplest approach and works well up to significant workloads. Horizontal scaling means adding additional servers and distributing work across them. This is appropriate for large deployments with many agents or very large knowledge bases. Most businesses operate comfortably on a single well-provisioned server for years before needing to consider horizontal scaling. See How to Scale Self-Hosted AI From One Server to Multiple for details.