How to Run an LLM Locally on Windows & Mac

Run a local LLM for private, low-latency inference—learn hardware checks, model choice, setup steps, and a clear implementation checklist to get started today.

Running a large language model locally gives you privacy, predictable latency, and offline capability. This guide walks through deciding whether to host locally, verifying hardware/OS compatibility, choosing a model and precision, preparing files, and step-by-step Windows and Mac instructions.

• Quick answer to whether local hosting makes sense and the fastest route to a working setup.
• Practical hardware/OS checks and model/runtime recommendations for consumer machines.
• Concrete step-by-step install, quantize, run commands, plus pitfalls and a short implementation checklist.

Quick answer

Yes—if you need privacy, low latency, or offline access and have a modern GPU (or accept CPU-only tradeoffs), you can run many LLMs locally using quantized weights and a lightweight runtime (llama.cpp, GGML-based runtimes, or PyTorch with CPU/GPU). For best balance of speed and resource use, choose a quantized model (4-bit/8-bit) and a runtime that matches your OS and GPU capabilities.

Decide whether to run an LLM locally

Local hosting benefits: data privacy, faster inference without network hops, predictable cost, and full control over updates and extensions. Downsides: hardware requirements, maintenance burden, and limited model size or latency if you lack a GPU.

• Prefer local if you process sensitive data, need offline operation, or require deterministic latency.
• Prefer cloud if you need the largest models (multi-100B), quick scaling, or minimal maintenance.
• Hybrid option: run smaller local models for common queries and offload heavy tasks to cloud models.

Verify hardware & OS requirements (Windows & Mac)

Check CPU, RAM, disk, and GPU compatibility before choosing a model and runtime. Below are practical minimums and recommended specs for useful local LLM work.

OS notes:

• Windows: WSL2 + GPU passthrough (NVIDIA) or native GPU drivers work well; ensure CUDA/DirectML support for chosen runtime.
• Mac: Apple Silicon (M1/M2) can run efficient models via Metal-native runtimes (llama.cpp, GGML builds). Intel Macs can run CPU or external GPU setups but expect slower speeds.
• Disk: keep at least 2x model size free for downloads and temporary quantization files.

Choose a model, precision, and runtime

Pick a model size that fits memory limits and desired accuracy. Precision (float32, float16, int8, 4-bit) affects memory and speed; lower precision is faster but slightly lower fidelity.

• Model families: Llama 2, MPT, Falcon, and open-source LLMs each have tradeoffs in license, performance, and toolchain support.
• Precision: float32/16 for highest quality; int8/4-bit quantized formats (GGML, Q4_0/Q4_K_M, etc.) for best speed and low memory.
• Runtimes: llama.cpp / GGML for CPU & Metal; vLLM / Transformers + bitsandbytes for GPU; Ollama (macOS) offers a simpler app-like experience.

Install prerequisites (drivers, WSL, Python, Homebrew)

Install and verify drivers and tools specific to your OS and chosen runtime before downloading models.

Windows

• Install WSL2 (optional): enable WSL, install Ubuntu from Microsoft Store, set WSL2 as default. Useful for Linux-native runtimes.
• NVIDIA GPU: install latest Game Ready or Studio drivers and CUDA toolkit if using CUDA-based runtimes; verify nvidia-smi.
• Install Python 3.10+ and pip; use virtualenv or conda for isolated environments.

Mac

• Apple Silicon: install Homebrew, then install dependencies. Use Metal-enabled builds (llama.cpp with metal backend) for best performance.
• Intel Mac: Homebrew + Python; expect slower CPU-only inference unless using external GPU solutions.
• Verify Homebrew with brew --version and Python with python3 --version.

Download, quantize, and prepare model files

Obtain model weights from the model distributor (obey licenses). Convert or quantize to a runtime-friendly format to reduce memory and increase throughput.

• Download: use official release pages or model hubs. Verify checksums when available.
• Convert: many models provide converters (e.g., Hugging Face -> GGML). Use the correct converter for your runtime.
• Quantize: run quantization scripts (e.g., llama.cpp quantize utility, or bitsandbytes quantization). Keep original files until you confirm the quantized model works.

Run the model: Windows & Mac step-by-step

Below are concise example flows: a typical Windows (WSL/CUDA) path and a Mac (Apple Silicon + llama.cpp) path. Adjust paths, model names, and flags to your environment.

Windows (WSL + CUDA) quick flow

1. Open WSL terminal (Ubuntu) and create venv:

   python3 -m venv llm-venv && source llm-venv/bin/activate

2. Install basics:

   pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

   (match CUDA version).

3. Install transformers & runtime:

   pip install transformers accelerate

   or follow runtime docs (vLLM, gguf support).

4. Place quantized model in folder and run a sample script:

   python run_inference.py --model ./models/your-model

5. For llama.cpp builds on WSL, build from source, copy GGML .bin and run:

   ./main -m ./models/model.ggml.bin -p "Hello world"

Mac (Apple Silicon) quick flow

1. Install Homebrew, then dependencies:

   /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

2. Clone and build llama.cpp with Metal support:

   git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make

3. Download or convert a GGML model to a .bin compatible with llama.cpp.
4. Run:

   ./main -m ./models/model.ggml.bin -p "Summarize the following:"

5. For Python-based deployments, install native wheels if available or use Conda with CPU builds.

Example minimal inference command (llama.cpp):

./main -m ./models/7B.ggml.q4_0.bin -n 128 -b 8 -p "Write a haiku about rain"

Common pitfalls and how to avoid them

• Insufficient RAM/VRAM — Avoid by choosing smaller model or higher quantization; monitor usage with nvidia-smi or Activity Monitor.
• Mismatched drivers/CUDA versions — Match CUDA toolkit and GPU driver; check runtime docs for supported versions.
• Wrong file formats — Verify you converted to the runtime’s expected format (GGML/GGUF vs PyTorch).
• Slow token generation — Use batching, shorter context windows, or quantized models; consider compiled runtimes like llama.cpp.
• License noncompliance — Confirm model license allows local hosting and your intended use.

Implementation checklist

• Decide local vs cloud based on privacy, latency, and scale needs.
• Verify hardware (CPU cores, RAM, disk, GPU VRAM) and OS compatibility.
• Choose model family, size, and quantization level.
• Install OS prerequisites: drivers, WSL (Windows), Homebrew (Mac), Python env.
• Download model weights and verify checksums.
• Convert/quantize to runtime format and keep originals as backup.
• Run sample inference and measure latency/memory; iterate on model/precision.
• Document setup and license terms; secure model files and runtime environment.

FAQ

• Q: Can I run a 70B model on a consumer GPU?
  A: Not typically; 70B models usually need >40–80 GB VRAM or multi-GPU setups. Use quantized smaller models or cloud for large models.
• Q: Is quantized output noticeably worse?
  A: Modern 4-bit/8-bit quantization often preserves useful quality for many tasks; evaluate on your prompts.
• Q: Which runtime is simplest for macOS?
  A: llama.cpp or Ollama provide the easiest paths for Apple Silicon with Metal support.
• Q: How to keep the model secure on disk?
  A: Use disk encryption, restrict filesystem permissions, and track who can access model folders.
• Q: Where to find model converters?
  A: Check the model hub repository or runtime project (llama.cpp, Hugging Face transformers) for official converters and scripts.