Private LLM Deployment: Run AI Without Sending Your Data to the Cloud
When cloud AI isn't an option — architectures for running large language models on your own infrastructure.
Not every organisation can send its data to OpenAI or Anthropic. Healthcare records, legal documents, financial data, and proprietary intellectual property often face regulatory constraints, contractual obligations, or competitive sensitivity that makes cloud AI a non-starter. Private LLM deployment — running models on your own infrastructure — solves this. The tradeoffs are real but manageable, and the options have improved dramatically in the past twelve months.
Why Private Deployment
The primary drivers for private LLM deployment are data privacy, regulatory compliance, and competitive sensitivity. GDPR and HIPAA create genuine constraints around sending personal data to third-party processors. Financial services firms face strict data residency requirements. Law firms cannot risk sending privileged communications through external APIs. For these organisations, private deployment isn't a preference — it's a requirement.
Beyond compliance, there's a cost argument at scale. Cloud API costs scale linearly with usage. At high query volumes, the economics of running your own infrastructure often become favourable, especially for predictable, high-volume workloads.
Model Options for Private Deployment
The open-weight model ecosystem has matured significantly. Meta's Llama family (3.1 and 3.3) offers near-frontier capability in the 8B–70B parameter range. Mistral's models are particularly strong for European deployments given the company's French origins and GDPR orientation. Qwen from Alibaba and DeepSeek offer competitive alternatives with strong multilingual capability.
For most private deployment use cases, 7B–13B parameter models running on a single GPU server offer the right capability-to-cost tradeoff. 70B models on multi-GPU setups match or approach GPT-4 quality on many tasks. Quantised versions (4-bit GGUF format) reduce memory requirements dramatically with modest quality tradeoffs.
Infrastructure and Serving
The standard serving stack for private LLMs uses vLLM or Ollama as the inference engine, with an OpenAI-compatible API layer that allows existing integrations to work without modification. vLLM's PagedAttention dramatically improves throughput for concurrent requests — essential for multi-user deployments. Ollama prioritises ease of use and is excellent for smaller teams or development environments.
Hardware decisions depend on model size and latency requirements. NVIDIA A100/H100 GPUs are the gold standard for production. For cost-sensitive deployments, RTX 4090 or AMD MI300 GPUs offer competitive inference performance at lower cost. CPU-only inference with llama.cpp is viable for 7B models on high-core-count servers, at the cost of higher latency.
Fine-tuning and Customisation
Private models can be fine-tuned on your organisation's data to improve performance on domain-specific tasks. Parameter-Efficient Fine-Tuning (PEFT) methods — particularly LoRA and QLoRA — make fine-tuning accessible without requiring massive compute: a 7B model can be fine-tuned on a single A100 in hours. The result is a model that understands your terminology, follows your style guidelines, and performs better on your specific task distribution.
Private fine-tuned models also enable capabilities cloud models can't match: training on your proprietary data without it ever leaving your infrastructure, building models that reflect your organisation's specific knowledge and judgment, and continuous improvement from your own usage data.