How to Run LLMs Locally: A Complete Step-by-Step Guide

Unlocking AI Power on Your Own Hardware

What Is a Large Language Model (LLM)?

An LLM is a neural network trained on massive text datasets. It works by breaking down your input into tokens (words or parts of words), processing them through billions of parameters (weights), and generating responses—a process called inference.

• Open models: Weights/parameters are publicly available (e.g., LLaMA, Gemma, DeepSeek, Mistral, Qwen).
• Proprietary models: Only accessible via cloud APIs (e.g., ChatGPT, Gemini); you can't download or run them yourself.

Benefits of Running LLMs Locally

• ✅ Simple learn AI and start: You can start learning AI and begin using it very quickly..
  • LLM Learning Roadmap for software developers
• ✅ Complete data privacy: Prompts and outputs remain on your machine.
• ✅ No vendor lock-in: You control the model version and updates.
• ✅ Offline-first AI: No internet connection is required after setup.
• ✅ Cost savings: No usage fees—only your hardware investment.
• ✅ Full control: Customize, update, and integrate as you wish.

Common Use Cases

• Private chatbots: Secure assistants for personal or business use.
• Document summarization: Summarize PDFs, Word files, or text files.
• OCR and data extraction: Extract text and information from images and scans.
• Code generation: Write or review code without sending it to the cloud.
• Local AI integration: Embed AI into your own desktop tools or automations.

Choosing the Right LLM

Popular open models include:

Always check the license—some may have restrictions on commercial use or require attribution.

Hardware Requirements

• CPU vs. GPU: You can run LLMs on a CPU (slower) or a GPU (much faster).
• RAM/VRAM: The model and context must fit in memory. For example, a quantized 7B model may need ~4GB, while a 13B model may need ~7GB. Larger models (70B+) can require 35GB or more.
• Apple Silicon: Uses unified memory, which is shared between CPU and GPU, making it easier to run larger models if you have enough RAM.

Even without a GPU, you can run smaller models, just at slower speeds.

✅ Hardware Options for Running Local LLMs (2025)

What is GGUF?

GGUF (GPT-Generated Unified Format) is a modern binary file format designed for efficiently storing and running large language models (LLMs) locally. It succeeds older formats like GGML, offering a single-file solution that includes all necessary metadata, model weights, and tokenizer information. GGUF is optimized for fast loading, memory mapping, and supports various quantization types, making it practical for running LLMs on consumer hardware. It is widely supported by tools like llama.cpp, LM Studio, Ollama, and Llamafile, enabling users to easily download, configure, and use advanced AI models on their own computers without relying on cloud services.

What Is Quantization?

Quantization compresses a model by reducing the precision of its weights:

• int4, int8: Store each parameter in 4 or 8 bits instead of 16/32 bits.
• Benefits: Dramatically reduces memory requirements with minimal loss in output quality.
• Formats: Look for quantized models (e.g., Q4, Q8) when downloading for local use.

GGUF Quantization Naming Matrix

Detailed Breakdown

1. Base Quantization (Q):

• Q4: 4-bit quantization (~4x compression)
• Q5: 5-bit quantization (~3.2x compression)
• Q8: 8-bit quantization (~2x compression)

2. K-Means Optimization (K):

• Uses cluster analysis to preserve critical weights
• Reduces quality loss compared to basic quantization

3. Group Size Suffix (S/M/L):

Common Configurations

Key Examples:

• Q4_K_M: 4-bit with medium groups → Best size/quality balance
• Q5_K_S: 5-bit with small groups → Faster than Q5_K_M with minimal quality loss
• Q3_K_L: 3-bit with large groups → Better than Q4_0 despite lower bit depth

Pro Tip: For most local deployments, start with Q4_K_M (4.5bpw) then test smaller formats if needed. Avoid Q2_K and unoptimized formats like Q4_0. Always use quantized models for local inference—they are much more practical for consumer hardware.

Tools to Run LLMs Locally

LM Studio and Ollama are the most accessible for beginners and power users alike.

Setting Up LM Studio

1. Download and Install Download the LM Studio installer for your OS and follow the setup steps.

2. Download Models

• Use the built-in Model Search to find quantized models (Q4/Q8).
• Pick a model suited to your hardware (smaller models for less RAM).
• Download—the model will be saved locally.

3. Use the GUI and API

• Load the model and start a new chat.
• Attach documents or images for summarization or OCR (if the model supports it).
• Optionally, enable the API server (in Power User or Developer mode) for programmatic access.

Example: Summarizing a PDF Attach a PDF and ask, "Summarize this document." The model processes the file locally.

Example: OCR from an image Attach an image and prompt, "Extract the total amount and date from this receipt."

Setting Up Ollama

Download and Install

Install Ollama for your platform.

Pull and Run Models via CLI

ollama pull llama3.2
ollama run llama3.2

• Replace llama3.2 with your chosen model (e.g., qwen3 for the Qwen model).
• Use the CLI for interactive chat or integrate with scripts.
• Popular models List

Programmatic Access Ollama exposes an API for use in your own applications.

Optimise for hardware

Example settings.

$ cat /etc/systemd/system/ollama.service.d/override.conf
[Service]
# Network
Environment="OLLAMA_HOST=0.0.0.0"

# CUDA Configuration
Environment="OLLAMA_USE_CUDA=1"
Environment="CUDA_VISIBLE_DEVICES=0"
Environment="LD_LIBRARY_PATH=/usr/local/cuda/lib64"

# Performance
Environment="OLLAMA_FLASH_ATTENTION=1"
Environment="OLLAMA_MAX_LOADED_MODELS=2"
Environment="OLLAMA_KV_CACHE_TYPE=q4_0"
Environment="OLLAMA_NUM_PARALLEL=8"  # Matches RTX 4060 Ti's 8 SM partitions

Environment="OLLAMA_MOE_EXPERTS=2"  # For 4060 Ti's 8 SM partitions

Note: that this settings should be in service configurations if Ollama configured as service.

Performance Tuning Tips

• Choose the right model size: Smaller models run faster and need less memory; larger models are smarter but require more resources.
• Prompt engineering: Clear, specific prompts improve results. Use examples (few-shot prompting) for better outputs.
• Manage memory and latency: Monitor RAM/VRAM usage in LM Studio. Adjust context window size as needed—larger windows allow more context but use more memory.
• Advanced settings:
  • Temperature: Controls randomness (lower = more predictable).
  • top_k/top_p: Limit candidate tokens for more focused or creative responses.
  • Flash Attention/KV Cache: Enable for faster generation if supported.

Risks and Limitations

• Legal: Always check and comply with model licenses, especially for commercial use.
• Performance: Older or low-end hardware may struggle with larger models.
• Updates & Model Management: Stay aware of new releases and manage disk space for downloaded models.

Conclusion

Running LLMs locally empowers you with private, cost-effective, and customizable AI. From chatbots and document analysis to code generation and automation, local LLMs unlock a world of possibilities—no cloud required.

Explore, experiment, and discover how local AI can transform your workflow!

Published on 5/27/2025