That risk isn't hypothetical. In Cisco's 2024 Data Privacy Benchmark Study, 69% of security professionals surveyed cited legal or IP harm as a concern, and 68% worried that information entered into GenAI tools could be shared publicly or with competitors.
A local LLM solves this differently. It's a language model that runs inference on hardware you control, a laptop, workstation, or on-premises server, instead of a provider's cloud. This article covers what local LLMs are, why businesses choose them, what hardware you need, and how to run your first model.
Key Takeaways
- A local LLM processes prompts on hardware you control—not a hosted provider's servers.
- Privacy depends on the full setup: logs, plugins, retrieval sources, and network access—not only model location.
- Memory (VRAM or RAM) is usually the first hardware constraint.
- No single "best" local LLM exists; match the model to your task, hardware, and data sensitivity.
What Is a Local LLM?
What "Local" Means in Practice
A local LLM performs inference on a device or infrastructure that the user or organization controls. You download model weights and run them through an inference tool. No foundation-model training is required.
The basic components:
- Model weights — the trained parameters
- Inference runtime — the software that loads and executes the model
- Hardware accelerator — GPU, CPU, or unified memory
- Application layer — optional interface layer
- Prompts and business data — the inputs that stay under your control
Info-Power's AI-ABW platform illustrates this structure directly. It runs Google's Gemma 4 via llama.cpp, with Open Web UI as the browser interface, entirely inside the customer's environment.
How Local Differs From Cloud and Private Deployments
| Deployment | Where inference runs | Internet required? | Who operates it |
|---|---|---|---|
| Local | User/organization hardware | No | You |
| Cloud API | Provider's data center | Yes | Provider |
| Private cloud | Dedicated, isolated instance (on or off premises) | Sometimes | Provider or you |
| Air-gapped | Fully offline hardware | Never | You, manually |
Here's the catch: "local" doesn't automatically mean "secure." Cloud connectors, telemetry, browser plugins, and backup services can still create data egress even when the model itself runs on your machine. Review every integration, not just where the weights live.
Running a Model vs. Customizing One
Most organizations don't need to train a model from scratch. Instead, they choose between:
- Inference — running an existing model as-is
- Prompt engineering — crafting inputs to get better outputs
- Retrieval-augmented generation (RAG) — connecting approved documents so the model can reference current information
- Fine-tuning — retraining part of the model on your own data
For most business use cases, an instruction-tuned model plus RAG covers the need. Before commercial use, always check the license and model card. "Open-weight" doesn't always mean "open-source," and terms vary by model family.
Why Run an LLM Locally?
Control over data movement is the strongest business case. A local deployment keeps prompts, retrieved documents, and outputs inside your network boundary.
That matters for law firms protecting privilege, healthcare organizations bound by HIPAA, and manufacturers guarding proprietary process data.
But local deployment isn't a compliance guarantee by itself. It supports a controlled data boundary; you still need access controls, logging policies, and patch management.
Local deployment also brings operational advantages:
- Offline access when connectivity drops
- No per-query or per-token fees
- No sudden model retirement by a vendor
- Reduced exposure to rate limits
The tradeoff: you now own the maintenance. Hardware refreshes, driver updates, and model version management fall on your team (or your vendor's support contract).
The Real Cost Comparison
Don't assume local AI is always cheaper. Gartner has flagged low GPU utilization as a common cost problem in on-premises AI deployments. Build a workload-based comparison instead:
- Requests per day and token volume
- Hardware purchase/lease cost
- Electricity and cooling
- Support and administration labor
- Refresh cycle timing
AI-ABW sidesteps some of this complexity with a flat, fixed-environment licensing model and no per-query or token fees. Costs stay predictable whether your team asks ten questions a day or ten thousand.
What Do You Need to Run a Local LLM?
Hardware and Memory Requirements
Available memory, GPU VRAM, system RAM, or unified memory, is usually your first constraint. Model weights, the context window, and the key-value cache all need to fit.
Rough starting points (verify against current model cards):
- Small quantized models (7B, Q4): roughly 4.5-8 GB
- Medium models (13B): around 16 GB RAM
- Larger models (70B): 64 GB or more
These figures come from llama.cpp's quantization documentation and older Ollama guidance. Treat them as screening rules, not guarantees, and check the exact model card before committing hardware.
CPU, GPU, Unified Memory, and NPU Options
- CPU-only works for smaller models but runs slower
- Discrete GPU speeds things up significantly if the model fits in VRAM
- Apple unified memory shares RAM between CPU and GPU efficiently
- NPUs are emerging as a dedicated option for some inference paths
Storage, OS, and Supporting Software
You'll need SSD storage for model files, compatible drivers, and headroom for multiple models or larger context windows. Quantization reduces file size and memory needs by lowering weight precision, common formats include GGUF, though speed and output quality can shift slightly depending on the quantization level chosen.
Once your hardware and storage are sorted, the next decision is which runtime actually loads and serves the model.
The Software Stack
- Ollama — beginner-friendly, command-line based
- LM Studio — graphical interface, no manual runtime tuning
- llama.cpp — lower-level control over backends and quantization
- vLLM — built for shared, multi-user serving
AI-ABW runs on llama.cpp because it gives fine-grained control over quantization and backend selection, letting deployments fit standard office or field hardware instead of requiring a data center.
Preflight Checklist
Before downloading anything, confirm:
- Intended task and data sensitivity
- Model license terms
- Available hardware memory
- Required context length
- Number of concurrent users
- Whether you need tool calling, vision, or RAG
How to Run an LLM Locally
Running an LLM on your own hardware follows a clear sequence: choose a model, install a runtime, load and test it, then connect it to apps or internal documents as needed.
Choose a Task and a Suitable Model
Match the model to the job—Q&A, summarization, or code—then start with a small, reputable instruction-tuned model that fits your available memory. Verify the model's license, format, and download source before you proceed.
Install a Local Runtime
For Linux/macOS with Ollama:
curl -fsSL https://ollama.com/install.sh | sh
Start the runtime with ollama serve and confirm it responds. Windows users can grab the official installer from Ollama's site. LM Studio offers a graphical alternative that downloads and serves models without manual configuration.
Download, Load, and Test the Model
Pull and run a small model, then send it a test prompt:
ollama pull llama3.1
ollama run llama3.1
Check whether the model is using your intended GPU or CPU, and watch memory usage and response speed as you test.
Connect the Model to an Application
Most runtimes expose an OpenAI-compatible local endpoint. Ollama's default is http://localhost:11434/v1/. That lets existing applications integrate without a full rewrite.
An API-compatible interface does not automatically make your application private. Check what the app itself logs or transmits.
Add Business Documents With Retrieval
A basic local RAG workflow:
- Ingest approved documents
- Chunk them into retrievable pieces
- Retrieve relevant passages per query
- Pass that context to the model
- Return source citations
AI-ABW uses this same pattern for internal knowledge. Operations manuals, SOPs, pricing guides, and HR policies are processed and stored on your own server. Nothing is sent externally, and answers stay grounded in your actual documentation.
Validate and Secure the Setup
Test with representative prompts for accuracy, hallucination rate, and response speed before relying on it for real work. Watch for:
- Insufficient memory or context overflow
- Slow responses or driver issues
- Unexpected network activity
Pin known-good model versions, restrict access to local endpoints, and disable unnecessary telemetry.
Choosing a Model and Planning a Responsible Deployment
How to Choose the Right Local Model
"Best" depends entirely on the use case:
- General-purpose models for broad Q&A
- Coding-focused models for development work
- Document/reasoning models for structured analysis
- Lightweight edge models for constrained hardware
Test candidate models against your own representative prompts and documents. Look at hallucination rate, tool-calling reliability, speed, and licensing, not just public leaderboard rankings.
Moving From One Computer to a Team Deployment
Scaling from a solo experiment to a shared service introduces new requirements:
- Concurrent-request capacity
- Authentication and role-based access
- Audit logs and model versioning
- Backup and monitoring procedures
This is where many DIY local LLM projects hit a wall.
Businesses that need controlled ERP querying, department-specific knowledge access, or secure workflows across manufacturing, distribution, legal, or healthcare operations often need more structure than a single laptop running Ollama can provide.
AI-ABW addresses this gap as a private business AI platform built on Info-Power's 30+ years of enterprise software experience. It runs entirely on customer-owned infrastructure or a dedicated private cloud instance.
Core capabilities include:
- Read-only access to business data
- Role-based knowledge controls
- No per-query fees
Architecture and compliance status vary by deployment, so evaluate your specific requirements against any vendor's actual setup.
Frequently Asked Questions
What is a local LLM?
A local LLM is a language model that runs inference on hardware you control instead of a hosted provider's servers. It differs from a cloud API in that data doesn't leave your environment by default, but overall privacy still depends on the full deployment, not just model location.
Why do people run LLMs locally?
Local deployment gives organizations control over data movement, offline access, and predictable costs without vendor rate limits. The tradeoff is that you take on hardware and maintenance responsibilities the provider would otherwise handle.
How do you choose an LLM to run locally?
There's no universal answer. It depends on your task, available memory, required context length, and licensing needs. Test a shortlist of models against your own real workloads before deciding.
Can I run a local LLM without a GPU?
Yes, smaller quantized models run fine on CPU alone. Larger models or faster response times generally benefit from a GPU or compatible accelerator.
How much RAM do I need to run a local LLM?
Requirements vary based on parameter count, quantization level, context length, and how many users run queries at once. Always check the specific model card and runtime documentation for exact figures.
