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What is local AI deployment?

Quick Answer

Local AI deployment means running AI models on your own servers or private infrastructure rather than using cloud-based API services. This gives you complete control over data flow, eliminates third-party data processing, ensures regulatory compliance for sensitive information, and provides predictable costs at scale. Open-source models like Llama, Mistral, and Phi make local deployment increasingly practical for businesses.

Summary

Key takeaways

  • Provides complete data sovereignty with no third-party data processing
  • Eliminates per-query API costs, offering predictable pricing at scale
  • Requires investment in GPU hardware or private cloud infrastructure
  • Open-source models have closed the performance gap with commercial alternatives

Benefits and Trade-offs of Local Deployment

Local AI deployment offers several compelling advantages. Data never leaves your infrastructure, which is essential for organisations handling sensitive personal data, classified information, or proprietary intellectual property. There are no per-query API costs, making it cost-effective at high volumes. You have complete control over model versions, updates, and configurations. There is no dependency on external service availability. However, local deployment requires significant upfront investment in GPU hardware, typically £10,000 to £100,000+ depending on scale. You need technical expertise to manage the infrastructure, handle model updates, and optimise performance. Models available for local deployment are generally smaller than the latest cloud-only frontier models, though the gap is narrowing rapidly.

Getting Started with Local AI

Start by identifying which workloads genuinely require local deployment and which can safely use cloud services. For workloads that need local deployment, evaluate open-source models on your specific use case. Models like Llama 3, Mistral, and Phi offer strong performance across a range of tasks. Test with representative data to ensure quality meets your requirements. For hardware, a single modern GPU server can handle many business AI workloads. Tools like Ollama, vLLM, and text-generation-inference simplify model serving. Consider starting with a smaller model to validate the approach before investing in larger hardware for bigger models. Many organisations use a hybrid approach, running sensitive workloads locally while using cloud APIs for less sensitive tasks.

Related

Related questions

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FAQ

Frequently asked questions

For smaller models (7-13B parameters), a single NVIDIA RTX 4090 or A6000 is sufficient. For larger models (70B+), you need multiple GPUs or enterprise cards like A100 or H100. Cloud GPU rental is an option for testing before purchasing hardware.

The latest open-source models perform comparably to GPT-4 on many business tasks. For complex reasoning and creative tasks, frontier cloud models still lead. However, for focused tasks like document processing and classification, local models are often sufficient.

Subscribe to model release announcements from providers like Meta, Mistral, and Microsoft. Test new model versions against your evaluation datasets before deploying. Use containerised deployment to make model swapping straightforward.

A single GPU server running AI inference typically consumes 500W to 1.5kW. Multi-GPU setups can consume 3kW to 10kW or more. Factor in cooling requirements which can add 30-50% to power costs. Annual electricity costs for a basic setup are typically £2,000 to £8,000.

Yes, but with limitations. Smaller models (up to 7B parameters) can run on modern CPUs with sufficient RAM, using quantised model formats. Performance is significantly slower than GPU inference but may be adequate for low-volume, non-real-time tasks.

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