Services / Local AI / Self-hosted LLM
Keep your AI workload — and your data — inside your perimeter.
When your data can't leave the perimeter.
No vendor lock-in. No token bills. No data leaving the perimeter. Llama, Qwen, Mistral, DeepSeek deployed via Ollama, vLLM, or llama.cpp on infrastructure you own. Fine-tuning, LoRA, and quantization (GGUF/AWQ/GPTQ) when the off-the-shelf model isn't enough. Local RAG with Qdrant, Chroma, or Milvus, and n8n/MCP integrations into the processes the model is meant to serve.
Right call when…
- →HIPAA, GDPR, SOC2, or internal policy blocks cloud LLMs.
- →Token costs are running away with your AI budget.
- →Latency or sovereignty requirements rule out remote APIs.
Questions people ask about Local AI / Self-hosted LLM
What hardware do I need for a self-hosted LLM?
Depends on model size and concurrency. A 7B-parameter quantized model runs acceptably on a single A100 or M-series Mac mini for low-volume use. We size hardware in the discovery phase.
Which open-source models do you work with?
Llama, Qwen, Mistral, DeepSeek, Gemma — via Ollama, vLLM, or llama.cpp. We pick based on your task, latency budget, and license constraints.
Who maintains the deployment after handoff?
Your team can, with our runbook and ongoing retainer if useful. We don't lock you into needing us forever.