Private AI and Local LLMs for Your Business

Yes, inference can run with no outbound network at all, once the model weights are on disk. That is a normal setup for air-gapped sites and for workstations that should never call a public API. Offline does not mean zero operations: you still need a way to bring model updates in, and you still need storage, backups and access control. Completely offline also means you cannot fall back to a cloud model when the local one is not good enough, so the workflow has to be chosen with that constraint in mind.

Yes. A local or private-cloud deployment keeps prompts and documents on infrastructure you control. That is the usual reason companies ask for this work. It is not a guarantee that nothing ever leaves the building: backups, admin laptops and logging can leak the same text if they are sloppy. The useful question is which path each class of data is allowed to take, and then building the system so the default path matches that policy.

It depends on the model size, how many people use it at once, and whether you also run speech-to-text. A single-user writing assistant can run on a well-specified workstation, often on CPU if the model is small and quantized. Team usage and larger models usually need a GPU with enough VRAM to hold the weights and the context. Transcription is a separate workload and often wants its own GPU time. Hardware is scoped in the audit against the actual workflow, not against a generic shopping list. The longer notes are in the article on local LLM hardware requirements.

Yes, for smaller models and low concurrency. CPU inference is slower and the quality ceiling is lower because you typically run a more aggressively quantized model. It is a reasonable choice for a personal drafting tool or for batch jobs that can wait. It is a poor choice if several people expect interactive chat against a 30B-class model. Measure tokens per second on the machine you actually have before you decide.

Not always. You need a GPU when the model, the context length or the concurrency will not fit a responsive CPU setup, or when you are transcribing a lot of audio. You do not need a GPU to prove that a workflow is useful: a small quantized model on a workstation is enough to test the prompts, the UI and the data flow. Buy or rent the GPU when the pilot has shown that the workflow is worth running all day.

Yes. Local speech-to-text, typically in the Whisper family, is one of the most reliable private AI workloads. It produces a transcript, often with timestamps, and a local LLM can then summarise, extract action items and make the text searchable. Speaker separation (diarization) is a separate step and is not always accurate, especially on overlapping speech or poor recordings. The pipeline is described in the local AI transcription article.

Yes, with retrieval-augmented generation: documents are parsed, split, embedded and indexed, then a local model answers using the retrieved passages. It is not a magic search engine. It fails on bad scans, on documents it has not ingested, on questions that need reasoning the retrieved text does not support, and on access-control mistakes. A usable system cites sources, refreshes the index when files change, and is evaluated on real questions, not on a demo PDF.

You do not pay per token. Completions on a local or self-hosted model do not appear on a vendor usage invoice. That is not the same as free: you still pay for the GPU or server, electricity or cloud rent, engineering time, and the cost of a worse answer that a person then has to fix. A few hundred API calls a month is almost always cheaper on a vendor. Continuous transcription of every meeting, or search over a large private corpus, can flip the other way because the token bill would have kept growing and the local one does not. The audit is there to do that sum with your numbers, not with a blog-post average.

Yes, if you size the inference server for concurrent requests and put authentication in front of it. A workstation model is one person. A shared on-premise or private-cloud server can serve a team, with the usual limits: queueing when everyone hits it at 9:05, and a GPU that can only hold so many sequences at once. Concurrency is a capacity-planning problem, not a feature checkbox.

Often, if that server has the RAM, disk and preferably GPU headroom, and if you accept that inference will compete with whatever else already runs there. Many companies start that way and move to a dedicated box or a private GPU instance when the workflow becomes daily. An existing application server with 16 GB of RAM and no GPU will not run a serious team assistant. The hardware review in the audit is specifically to avoid that surprise.

Local usually means a workstation or a server in your office or datacentre, with the weights on disks you own. Private cloud usually means a virtual machine or GPU instance in an account you control at a cloud provider: still not a public chatbot, still not a multi-tenant AI SaaS, but the hardware is rented and the data centre is not yours. Both can keep prompts off public AI APIs. The difference is who holds the keys to the machine, how you scale, and what happens if the network to that machine is down.

Yes, and that is usually the right design. Transcription is a speech model. Search uses an embedding model plus a generator. Drafting can use a larger instruct model. Extraction often wants a smaller model with a strict JSON schema. Mixing them behind one OpenAI-compatible API is ordinary. Forcing one model to do every job is how systems get both slow and sloppy.