Run Your Own Local LLM

Running a local LLM gives you full control of your data, which is critical for sectors that handle sensitive information. A law firm can review contracts, draft clauses, or summarise case notes without sending confidential documents to external servers. A finance team can analyse reports, prepare statements, or explore forecasting ideas while keeping client records completely offline. Everything stays inside your machine, protected by your own security policies. This setup delivers fast performance, total privacy, and a safer way to use AI for high trust industries that cannot risk leaks or exposure. You also don’t need a monthly subscription to use ChatGPT.

Choose your Own LLM Model

Running a local setup lets users pick the exact model that fits their workflow. Here are some of our favourite models, and their benefits.

Alibaba Qwen3 VL 30B

The latest generation vision-language MoE model in the Qwen series with comprehensive upgrades to visual perception, spatial reasoning, and video understanding.

Key Features
Visual Agent: Operates PC and mobile GUIs—recognizes elements, understands functions, and completes tasks
Visual Coding: Generates Draw.io, HTML, CSS, and JavaScript from images and videos
Advanced Spatial Perception: Provides 2D/3D grounding for spatial reasoning and embodied AI applications
Upgraded Recognition: Recognizes celebrities, anime, products, landmarks, flora, fauna, and more
Expanded OCR: Supports 32 languages with robust performance in low light, blur, and tilt conditions
Pure Text Performance: Text understanding on par with pure LLMs through seamless text-vision fusion
High-Efficiency MoE: 31.1B total parameters with only 3B activated (A3B) for excellent efficiency

OpenAI GPT-OSS 20B

Designed for lower latency and specialized or local deployment, the model has 21B total parameters with only 3.6B active at a time, capable of operating within 16GB of memory.

It features configurable reasoning effort—low, medium, or high, so users can balance output quality and latency based on their needs. The model offers full chain-of-thought visibility to support easier debugging and increased trust, though this output is not intended for end users. It is fully fine-tunable, enabling adaptation to specific tasks or domains, and includes native agentic capabilities such as function calling, web browsing, Python execution, and structured outputs.

Gemma 3 27B by Google

Optimized with Quantization Aware Training for improved 4-bit performance. Supports a context length of 128k tokens, with a max output of 8192. Multimodal supporting images normalized to 896 x 896 resolution. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning.

DeepSeek-R1-0528

DeepSeek has released a new iteration of the R1 model, named DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro.

Our team focuses on Qwen and GPT OSS because both offer strong performance, There are plenty of other models such as Mistral, Grok (XAI), Ilama (Meta), Granite (IBM), Seed (Bytedance).

Retrieval Augmented Generation (RAG)

RAG stands for Retrieval Augmented Generation. It is a technique that lets an AI model pull information from your own documents, notes, PDFs, or databases before generating an answer. Instead of relying only on what the model was trained on, RAG searches your files, retrieves the most relevant sections, and feeds them back into the model as context. This gives you answers grounded in your actual data rather than generic guesses. It is powerful for research, legal work, finance, customer support, and any workflow that needs accuracy tied to real sources.

Here’s how it works:

In a locally hosted LLM system, users can upload up to 5 files at a time, with a maximum combined size of 30MB. Supported formats include PDF, DOCX, TXT, JPEG, PNG and CSV.

System Prompt / Custom instructions

A system prompt is the set of core instructions that guides how an AI model should behave before any conversation begins. It defines tone, rules, boundaries, and the overall role the model must follow. Unlike a normal user prompt, the system prompt sits at the highest level and shapes every answer that comes after it. It can tell the model to act as a coding assistant, follow strict formatting, avoid certain styles, or maintain a specific workflow. It is essentially the model’s blueprint for how to respond.

Heres an example:

You are an AI assistant supporting a legal team. Provide clear, concise, and accurate information grounded strictly in the facts given. Prioritise confidentiality and avoid assumptions not supported by the provided documents. Explain legal concepts in plain language when needed, and structure answers in a professional format suitable for internal case notes. Do not generate legal advice, only assist with research, summarisation, and document analysis.

Custom Context length

It is the amount of text an AI model can read, remember, and use at one time while generating a response. It works like a sliding window that holds your conversation history, documents, code, or instructions. A longer context length lets the model handle bigger files, track longer discussions, and maintain consistency across complex tasks. A shorter one limits how much information the model can process before older details begin to drop out of memory.

People using the browser version of ChatGPT often run out of memory due to context length, for example if you paste in a long piece of code, or extended documents, the model starts to forget earlier sections, lose references, or produce incomplete code.

A locally run LLM system gives users full control over context length, letting you decide how much information the model can hold and work with. This is a major advantage compared to browser based AI tools where the context limit is fixed and cannot be adjusted. By expanding context length, you can load longer documents/code, maintain deeper conversations, and process entire reports or scripts in one go. Reducing it speeds up performance on smaller machines. This flexibility lets you tailor the model to your hardware and your workflow.

Example of our locally run LLM system. The user is asking the AI to describe the image. Look at the latency/speed of the replies. 

Summary

Running your own LLM locally gives you full privacy, since all data stays on your machine with no external transmission. You gain faster responses because the model processes everything directly on your hardware. You can choose different models that fit your workflow, from lightweight reasoning models to larger creative ones. You control context length, system settings, and optimisation levels. Local setups also allow tools like RAG, letting you feed your own documents into the model securely. It is a flexible, private, and powerful way to integrate AI into your work.

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Contact MT Research Labs if you’ll like us to incorporate and systemize a local LLM for your enterprise.