What are the hardware requirements for private AI?

Private AI often requires a modern GPU, sufficient RAM, and storage. The exact requirements depend on the models and workloads you plan to run.

What are the ongoing costs of running private AI?

Ongoing costs include electricity, hardware maintenance, software updates, and your time for technical upkeep.

Is private AI right for everyone?

Private AI is best for those with strong privacy needs, technical skills, and use cases that match local model strengths. Cloud AI may be better for convenience and collaboration.

Private AI Implementation Realities

SMB & individual readiness checklist before you over-engineer

Who This Is For
This checklist targets individual power users and small teams. Large enterprise requirements (SSO, audit, multi-tenant governance) are intentionally excluded—see the Enterprise Scaling Guide if you truly need them.

⚠️ Before You Start: Reality Check (Avoid Premature Complexity)

Most SMBs don't need a "stack"—they need a repeatable workflow: a local model (or two), a retrieval layer, and disciplined document curation. Treat everything else (agents, fine-tuning, distributed infra) as optional experiments, not table stakes.

💻 Hardware & Resource Requirements

GPU Requirements: Do you have access to a GPU with at least 8GB VRAM for running models like Llama-3 8B? (16GB+ recommended for larger models)
RAM Requirements: Is your system equipped with 16GB+ RAM? (32GB+ for optimal performance with larger contexts)
Storage Space: Do you have 50GB+ available for models, embeddings, and vector databases?
Performance Expectations: Are you comfortable with 5-30 second response times vs. 1-3 seconds for cloud models?

💰 True Cost Analysis

Upfront Hardware Investment:

RTX 4090 (24GB): $1,600-2,000
Mac Studio M2 Ultra: $4,000-6,000
High-end Linux workstation: $3,000-8,000

Ongoing Costs:

Electricity: $20-100/month (depending on usage)
Maintenance and upgrades: $200-500/year
Time investment: 2-10 hours/month for management

Compare to: GPT-4 subscription at $20/month or API usage at $0.01-0.06 per 1K tokens

🔧 Technical Maintenance Burden

Model Updates: Are you prepared to manually download, test, and deploy new model versions monthly?
Dependency Management: Can you troubleshoot Python environments, CUDA drivers, and framework version conflicts?
Data Management: Do you have a strategy for backing up embeddings, conversation history, and model configurations?
Security Maintenance: Will you keep up with security patches for your OS, Python packages, and AI tools?

Model Quality Reality Check

Use Case	Cloud Models (GPT-4/Claude)	Local Models (Llama-3/Mistral)	Recommendation
Local viable for most use cases
Code Generation	Excellent	Good	Local good for standard tasks
Complex Reasoning	Excellent	Limited	Cloud preferred for complex analysis
Document Summarization	Excellent	Excellent	Local ideal for this use case
Research & Analysis	Excellent	Good	Depends on depth required
Language Translation	Excellent	Limited	Cloud strongly preferred
Domain-Specific Tasks	Good	Excellent*	*With proper fine-tuning

🎯 User Experience Considerations

Setup Complexity: Are you comfortable with command-line tools, Docker, or technical documentation?
Interface Limitations: Can you work with simpler interfaces vs. polished cloud AI experiences?
Reliability Expectations: Will occasional crashes, memory issues, or restart requirements be acceptable?
Context Limitations: Can you work within smaller context windows (4K-32K tokens vs. 128K+ for cloud models)?

📊 Data Currency & Updates

Knowledge Cutoffs: Are you okay with models trained on data from 6-18 months ago vs. near real-time for cloud models?
External Data Integration: Do you have a plan for updating your local knowledge base with new information?
RAG Pipeline Maintenance: Can you manage embedding updates, vector database optimization, and retrieval tuning?

🚨 When Private AI May Not Be Right for You

Limited Technical Skills: If troubleshooting software feels overwhelming
Tight Deadlines: When you need maximum model capability for critical tasks
Team Collaboration: If you need shared context and multi-user access
Hardware Constraints: Working on older or resource-limited machines
High Availability Needs: When downtime isn't acceptable
Cutting-Edge Requirements: Need for the latest model capabilities

✅ Getting Started Checklist

If you've checked most boxes above, here's your implementation roadmap:

Start Small: Begin with Ollama and a 7B parameter model
Test Use Cases: Validate model quality for your specific needs
Measure Performance: Compare response times and accuracy
Build Gradually: Add RAG, better hardware, and orchestration over time
Plan Fallbacks: Keep cloud AI access for complex tasks

The Bottom Line

Private AI is powerful but not effortless. It requires:

Meaningful upfront investment (time and money)
Ongoing technical maintenance
Acceptance of quality/speed trade-offs
Clear understanding of your threat model

It's ideal when: Privacy concerns outweigh convenience costs, you have the technical capability to maintain it, and your use cases match local model strengths.

Consider cloud AI when: You need maximum capability, minimal maintenance, team collaboration, or lack the resources for local deployment.

← Back to "The Rise of Private AI" article (for advanced users)

Part of the Private AI article series by Josh Kaner