A local LLM is a large language model that runs on your own computer, so it can process prompts without sending them to a cloud chatbot. For a brokerage, that makes local AI useful for drafting and summarising documents when every processing step stays on the device. It still needs a secured computer and a broker who checks the result.
What a Local LLM is
A local LLM generates text using model files stored on the brokerage’s computer. An app loads those files into memory, takes your prompt and produces an answer. The model files contain learned numerical values called weights.
With a cloud chatbot, your prompt normally travels to the provider’s servers for processing. A fully local setup performs that work on your machine. Once the app and model are downloaded, an offline LLM can answer without an internet connection.
That boundary covers more than the final answer. A document workflow can also extract text, search passages and save chat history. Each stage must run locally if the aim is to keep client information off external services.
For example, a local chatbot can turn a broker’s de-identified notes into a draft document request. Connecting the same app to a cloud model changes where those notes go. The app’s name alone does not establish privacy.
Open Source, Open Weight and Local AI
Open source AI describes access and rights, open weights describe available model files and local AI describes where processing happens. These terms answer different questions about a brokerage’s setup.
| Term | What it tells you | What it does not establish |
|---|---|---|
| Open source AI | Under the Open Source Initiative’s definition, the system provides freedoms to use, study, modify and share, with the required code and training-data information. | That the system runs on your computer or suits confidential documents. |
| Open weight model | The trained weights are available to download under a licence. | That all training information is available or that every use is permitted. |
| Local AI | The selected processing runs on equipment you control. | That the app is open source or that connected services also run locally. |
The Open Source Initiative’s AI definition requires more than downloadable weights. Read the model’s licence separately from the app’s licence. Permission to install an app does not settle permission to use every model it can load.
ChatGPT is a hosted service, not an open source model you can install as a local copy. OpenAI separately distributes gpt-oss open-weight models under Apache 2.0, subject to its usage policy. Its gpt-oss documentation distinguishes those models from ChatGPT.
Qwen3 and Gemma 3 are other model families with downloadable weights for local use. Their licences differ. A familiar brand and an available download do not make the models interchangeable.
Can You Run AI Locally in a Brokerage?
Yes, you can run AI locally in a brokerage when the computer can hold the model and its working memory. Small models are the practical starting point for an office laptop. Large models and long documents need more memory and can make the machine slow to respond.
A supported operating system, spare disk space and enough memory matter before any hardware purchase. LM Studio recommends at least 16GB of RAM for supported Macs and Windows computers. Its system requirements specify Apple Silicon and macOS 14 or newer for Macs. An 8GB Mac is limited to smaller models and modest context sizes.
Context is the amount of material the model handles in a request, including the conversation and document text. A model that loads successfully can still run out of memory when you add a long statement. Quantisation compresses model weights to reduce memory use, with a possible effect on answer quality.
Local Model Apps
| App | Practical fit | Setup and licence |
|---|---|---|
| LM Studio 0.4.28 | A desktop chat interface for loading local models and working with documents. | Download the app and a compatible model. The app’s terms permit personal and internal business use, with separate model terms. |
| Ollama 0.40.15 | A local model runner with a desktop interface and command-line tools, useful when technical support will manage the setup. | The runner uses the MIT licence. Model licences remain separate, and cloud features must be disabled for a local-only workflow. |
LM Studio’s offline-operation guide explains which tasks work after model files are downloaded. Its local document processing can also work offline.
The Ollama FAQ documents local-only mode. It supports OLLAMA_NO_CLOUD=1 and a disable_ollama_cloud setting, followed by a restart. Keep its local service restricted to the computer unless your IT provider deliberately secures access for the office.
Installation is only part of the work. Someone must select compatible models, set storage permissions and maintain the app. Budget staff time for updates and for checking that a changed configuration still keeps processing local.
Choosing a Local Model for Broker Tasks
Choose a local model for the task and memory available, rather than choosing the highest-ranked model you can find. A short draft email needs less document handling than questions across a large client file.
| Broker task | Model or app capability to look for | Result the broker must check |
|---|---|---|
| Drafting from de-identified notes | An instruction-tuned text model that follows the requested audience and format. | The draft preserves the supplied facts and adds no promises or invented details. |
| Summarising a statement | Reliable text extraction and enough context for the selected pages. | Amounts, debit signs and recurring entries match the source. |
| Answering questions about documents | Local document search that returns the passages used in the answer. | Every answer is supported by the cited passage and belongs to the correct client file. |
| Reading a scanned page | A local image-capable model or local optical character recognition, which converts images to text. | The extracted rows match the page before the model interprets them. |
Qwen3-8B is a text-model example with an Apache 2.0 licence. Its model card documents support in local apps. Gemma 3 4B is a smaller image-and-text model example governed by Google’s Gemma terms. Image support also depends on the app and the model file you load.
Neither example establishes a universal best local LLM. The Qwen3-8B model card and Gemma 3 model card describe different models, capabilities and evaluations.
Rankings change as developers release models and change evaluation methods. A benchmark can measure reasoning while saying little about a bank statement with cramped columns. For broker work, accurate source extraction and traceable answers matter more than a general leaderboard position.
Where Local AI Falls Short
Local AI keeps processing under your control, but it can still misread a document or invent an answer. A confident explanation of a transaction is not evidence of its purpose. Use the source statement and the client’s explanation to resolve uncertainty.
Document search can also miss a relevant page. A model’s advertised context limit does not prove that your laptop can handle that amount of text comfortably. If the app omits pages or reports a memory error, use a smaller document selection and confirm which pages were included.
Local AI transfers maintenance work to the brokerage. Keep the operating system and model app patched. Restrict device access, encrypt storage and keep protected backups with a tested recovery process. Review whether cloud backup or sync software copies chat histories and extracted text elsewhere.
Treat documents as untrusted input. A page can contain instructions that try to redirect an AI assistant. Keep local AI agents away from sending emails, changing files or submitting applications while you assess document handling. Read-only summarising gives an error fewer ways to affect a client.
A local model also carries its training limits into the office. It cannot establish current lender policy from its memory. When the task needs a current rule, retrieve the authoritative document and assess the answer against that document.
A Private Workflow for Client Documents
A private document workflow keeps the source, extraction and model processing on approved devices, then checks the output against the original. Start with a redacted copy and one narrow question.
In this hypothetical example, a broker wants a summary of recurring entries from a bank statement. The statement contains two salary credits of $3,200 and four rent debits of $650.
- Make a working copy in the brokerage’s approved storage. Remove names, account numbers and other identifying details. Verify that the redacted text cannot be recovered by copying or searching the file.
- Download the approved app and model before opening the document. Select local processing and disable cloud features, web search and external tools. Run a harmless prompt with networking disconnected to confirm the model still answers.
- Extract the selected statement pages locally. Compare the extracted text with the visible rows, especially amounts and debit signs. If the PDF is a scan, use approved local text recognition and check its output first.
- Give the model a limited instruction: “List recurring credits and debits from this text. Quote each supporting row. Do not infer income eligibility or transaction purpose. Mark unclear entries for review.”
- Compare every returned entry with the working copy. In this example, the salary credits total $6,400 and the rent debits total $2,600. Calculate those totals separately and reject any answer that changes an amount or omits a row.
- Save only the checked summary in the client record, with a reference to its source. Handle temporary text and chat history under the brokerage’s retention policy. Keep the original statement available for the broker’s assessment.
If the model invents a transaction, remove the invented entry and review the rest of the output against the source. If it cannot show the supporting rows, complete the summary manually. A local LLM earns its place in the workflow when its checked output saves time without weakening the client record.
