Local AI (LLM) for companies: when it pays off and what hardware you need

For most companies, cloud AI (OpenAI, Anthropic, Google) under a business agreement is secure enough and the most cost-effective. But sometimes data simply cannot leave the company. Then the answer is local AI.

When it makes sense

• legal or contractual obligations (healthcare, finance, public sector, defense),
• highly sensitive documents (contracts, patents, personal data),
• very high request volumes where cloud cost becomes significant,
• offline or air-gapped environments.

Which models

Open models (Llama, Qwen, Mistral, DeepSeek, Gemma and others) are now very good at:

• answering from documents (RAG),
• summarization and data extraction,
• classification and routing,
• internal chat assistants.

For complex reasoning and coding, top cloud models are still ahead, so we often combine: sensitive data local, everything else through the cloud.

How much hardware you need

Roughly, for 4-bit quantized models:

• 7 to 8B parameters: 6 to 8 GB VRAM,
• 14B: about 10 to 12 GB,
• 30 to 32B: about 20 to 24 GB,
• 70B: about 40 to 48 GB (two GPUs or a server).

Concurrent users need extra memory for context. Check your machine with Can my computer run a local AI model.

Cloud or local: cost

Local AI has an upfront cost (server, GPU, setup) and a low monthly cost (power, maintenance). Cloud has no upfront cost, but you pay per token. The break-even point depends on volume; for companies with hundreds of users a day, local AI often pays off in 12 to 24 months. Estimate cloud cost with the LLM API cost calculator.

How we deploy local AI

We choose a model by task and language, set up a server with vLLM or Ollama, connect it to a RAG system and the Intrascope workspace, and add monitoring. See our AI document assistant and IT infrastructure maintenance services, or send an inquiry.