Can you run local LLMs for business?
Running a local LLM means the model itself runs on your own hardware — no prompt or response ever goes to a model vendor's API. For a business, that turns the language model from an external dependency into part of your own infrastructure.
Why businesses run local models
Section titled “Why businesses run local models”- Data never leaves — prompts and documents stay on your hardware, which is the foundation of air-gapped and high-compliance deployments.
- Predictable cost — you pay for hardware once instead of per token, which matters at high volume.
- No vendor lock-in — open-weight models can be swapped, pinned, and run for as long as you need them.
- Offline operation — agents keep working with no internet connection at all.
The trade-offs
Section titled “The trade-offs”Local models are not free of cost — they shift it. You need capable hardware (a GPU for anything beyond small models), and you take on the operations of running and updating the runtime. The most capable frontier models are still cloud-only, so for the hardest reasoning tasks a cloud API may still win. The pragmatic answer is rarely "all local" or "all cloud" but a mix.
How local models fit an agent platform
Section titled “How local models fit an agent platform”A model-agnostic agent platform points at a local runtime instead of a cloud API, so inference stays on your hardware. The strongest setups let you choose the model per agent: a local model for the agent that reads sensitive documents, a frontier cloud model for the agent that does open-ended research.
In Pinchy
Section titled “In Pinchy”Pinchy supports local models through Ollama alongside the cloud providers (OpenAI, Anthropic, Google), and the model is configurable per agent. Point an agent at a local model and its data never leaves the network; point another at a cloud API when you need maximum capability. See Set Up Local Ollama and Manage LLM Providers.