How to Run Llama 4 Locally: A Complete Step-by-Step Guide

Meta’s Llama 4 is an open-weight model that understands both text and images, and you can download it locally. When you run Llama 4 locally, your prompts and documents stay on your machine; there is no cost per request, and it works without an internet connection.

This guide covers the hardware you need, how to set it up with Ollama, Hugging Face Transformers, or LM Studio, and how to fix common errors. It also explains what to do if your computer cannot handle Scout, the lighter Llama 4 version.

Quick Answer:

To run Llama 4 locally, install Ollama and type ollama run llama4:scout in your terminal. The model downloads once, and after that, it works on your computer without an internet connection.

• Install Ollama: It is free and available for Windows, macOS, and Linux.
• Run the model: The command downloads Llama 4 Scout (about 67 GB) and opens a chat.
• Check your memory: Scout needs roughly 96 GB of RAM or unified memory. On a smaller computer, run Llama 3.1 8B with ollama run llama3.1:8b.

What Is Llama 4 and Why Does It Matter?

Llama 4 is Meta’s latest family of open-weight AI models, released in April 2025. It understands both text and images, and anyone can download it and run it on their own computer. Open weight means you can download the model files and run them on your own hardware.

It comes in two versions:

• Scout: The smaller, lighter version, built to be efficient and handle very long documents.
• Maverick: The larger and more powerful version, built for harder tasks, but it needs much more hardware.

Why Does It Matter?

• Multimodal: It reads images as well as text.
• Long context: Scout handles up to 10 million tokens at once, and Maverick up to 1 million.
• Multilingual: It supports 12 languages, including English and Hindi.
• Efficient design: Only 17 billion parameters work at a time, but the full model must still fit in memory.

Llama 4 is one of many open models changing how AI gets built. If you want to understand how these models work and build with them yourself, explore HCL GUVI’s Intel & IITM Pravartak Certified AI/ML Course, with live classes and no coding background needed.

Why Run Llama 4 on Your Own Computer?

Using an AI model through a website means your data travels to someone else’s servers. Running Llama 4 on your own computer avoids this and offers several other clear benefits.

• Privacy: Your prompts and documents stay on your machine. This matters for financial records, medical details, and client data.
• Cost: After the download, there is no charge per request or per token.
• Availability: It works without an internet connection, so it keeps running during an outage or while you travel.
• Control: You choose the settings and can fine-tune the model on your own data.

Note: There is one trade-off. Llama 4 needs powerful hardware, so a hosted service may cost less for occasional use. Local setup is worth it when privacy, offline use, or control matters most.

Also Read: How to Use llama.cpp to Run LLaMA Models Locally

Hardware Requirements for Llama 4

Memory is the main limit. The whole model must fit in your GPU memory, your system RAM, or the unified memory of an Apple Silicon Mac.

The table below shows what to plan for. It includes older Llama models, because Llama 4 Scout is too large for most personal computers.

These figures are approximate. A long context window needs extra memory on top of the model files, so leave some headroom.

Ollama and LM Studio both run on macOS, Windows, and Linux. On a Mac, you need an Apple Silicon chip, because LM Studio does not support Intel Macs.

Use an SSD for storage and keep at least 100 GB free if you plan to try Scout. A GPU makes responses much faster. Without one, only the small models are comfortable to use.

Quantization Explained in Simple Words

Quantization stores the model’s numbers with fewer bits. The files become smaller and need less memory, but answer quality drops slightly as the bit count falls.

The sizes below are for Llama 4 Scout.

Q4_K_M is the version that Ollama downloads for Scout by default. Choose the highest level that fits in your memory with some room to spare. Keep the very low-bit versions for cases where nothing else fits.

Run Llama 4 Locally with Ollama (Easiest Method)

Ollama is a free tool that downloads models, runs them, and offers a local API. It handles the technical work, which makes it the easiest place to start.

Step 1: Install Ollama

On macOS, download the app from ollama.com, move it to the Applications folder, and open it once.

On Windows, download and run OllamaSetup.exe from ollama.com. Ollama runs as a native Windows app, so WSL isn’t needed, and the installer doesn’t require administrator rights.

Windows needs version 10 22H2 or newer. If you use an NVIDIA card, install driver version 551.61 or newer.

On Linux, run this command in a terminal:

curl -fsSL https://ollama.com/install.sh | sh

Then confirm that the installation worked. Keep Ollama updated, because Llama 4 needs a recent version.

ollama --version

Step 2: Download and Run the Model

This single command downloads Scout and starts a chat:

ollama run llama4:scout

The download is about 67 GB, so it will take time. If you prefer to download first and chat later, use ollama pull llama4:scout.

Maverick uses the tag llama4:maverick, but it needs far more memory than a normal computer has.

If your computer is smaller, choose one of these older models instead:

ollama run llama3.2        # 3B model, about 2 GB
ollama run llama3.1:8b     # 8B model, about 5 GB
ollama run llama3.3:70b    # 70B model, about 43 GB

Step 3: Chat with the Model

When the prompt appears, type a question and press Enter. For example:

>>> Explain the difference between supervised and unsupervised learning.

Type /bye to leave the chat. Meta recommends a temperature of 0.6 and a top-p of 0.9 for Llama 4. You can set the temperature inside the chat:

/set parameter temperature 0.6

Step 4: Use the Local API

Ollama also serves an API at http://localhost:11434. Your own applications can call it, so you can build tools on top of the model without any external service.

On macOS and Linux, use curl:

curl http://localhost:11434/api/generate -d '{
  "model": "llama4:scout",
  "prompt": "What is machine learning?",
  "stream": false
}'

On Windows, use PowerShell:

(Invoke-WebRequest -Method POST -Body '{"model":"llama4:scout","prompt":"What is machine learning?","stream":false}' -Uri http://localhost:11434/api/generate).Content | ConvertFrom-Json

Also Read: Building Visual RAG Pipelines with Llama 3.2 Vision and Ollama

Your own Llama model is now answering through a local API, which is the foundation of real AI applications. Learn to turn that into chatbots, RAG systems, and deployed APIs with HCL GUVI’s Intel & IITM Pravartak Certified AI/ML Course, guided by live mentors and 20+ projects.

Run Llama 4 with Hugging Face Transformers

This method suits Python developers who have access to a GPU server. The original BF16 weights of Scout are about 217 GB. Meta states that Scout fits on a single H100 GPU when it is quantized to int4.

For a normal desktop or laptop, Ollama or LM Studio is the better choice.

Step 1: Request Access

Llama 4 is a gated model. Open the page meta-llama/Llama-4-Scout-17B-16E-Instruct on Hugging Face, accept the terms, and submit the access form. Then wait for approval.

Step 2: Install the Libraries

Llama 4 needs Transformers version 4.51.0 or newer.

pip install -U transformers torch accelerate huggingface_hub

Step 3: Log In and Download

The Hugging Face command line tool is now called hf. The older huggingface-cli command still works but shows a deprecation warning.

hf auth login
hf download meta-llama/Llama-4-Scout-17B-16E-Instruct --local-dir ./llama4-scout

Paste your access token when asked.

Step 4: Run the Model in Python

import torch
from transformers import AutoProcessor, Llama4ForConditionalGeneration

model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct"

processor = AutoProcessor.from_pretrained(model_id)
model = Llama4ForConditionalGeneration.from_pretrained(
    model_id,
    attn_implementation="flex_attention",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

messages = [
    {
        "role": "user",
        "content": [{"type": "text", "text": "What is Llama 4?"}],
    }
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
response = processor.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])[0]
print(response)

Run the script, and the answer prints in your terminal.

Run Llama 4 with LM Studio

LM Studio is a desktop app, so you don’t need a terminal. It suits people who prefer a visual interface.

On macOS, it needs an Apple Silicon chip and macOS 13.4 or newer; 16 GB of RAM or more is recommended.

On Windows, it needs an x64 or ARM processor with AVX2 support, 16 GB of RAM, and a GPU with at least 4 GB of VRAM.

On Linux, it needs Ubuntu 20.04 or newer and is distributed as an AppImage.

To run Llama 4, follow these steps:

1. Install LM Studio and open it.
2. Search for llama-4-scout-17b-16e-instruct in the model search.
3. Pick a Q4_K_M version and check its file size against your memory.
4. Download it, load it, and start a new chat.

After the download, everything runs on your computer. If Scout is too large for your machine, search for a smaller Llama model in the same way.

Tips to Improve Speed

Once Llama 4 is running, the next goal is faster responses. Most slowdowns come from memory and hardware, so a few small changes can make a big difference.

• Use your GPU: A GPU is much faster than a CPU for running these models. Keep your NVIDIA drivers up to date. Ollama also supports AMD Radeon GPUs on Windows, and Apple Silicon Macs use their GPU automatically.
• Keep the whole model in fast memory: Speed depends mostly on memory bandwidth. If the model spills from VRAM into system RAM, responses become much slower.
• Lower the context length: A long context window needs a lot of extra memory. Use a smaller value unless you really need the full length.
• Use an SSD: The model loads from disk into memory at startup, and an SSD makes this step faster.
• Close other programs: Free memory directly improves performance.

Common Errors and How to Fix Them

Even with the right setup, you may run into a few errors. Most of them have simple causes and quick fixes.

• Out-of-memory error: The model is too large for your computer. Use a smaller model, a lower quantization level, or a shorter context length, and close other programs.
• Very slow responses: The model is probably running on the CPU. Run ollama ps to check whether it is loaded on the GPU, and update your GPU drivers.
• Model not found in Ollama: Check the tag for typing mistakes, such as llama4:scout. Run ollama list to see what is installed, and update Ollama to the latest version.
• Access denied on Hugging Face: Make sure you submitted the access form and received approval. Then log in again with hf auth login.
• Unknown model type “llama4” in Transformers: Your Transformers version is too old. Run pip install -U transformers to get version 4.51.0 or newer.
• The hf command is not found: Update the library with pip install -U huggingface_hub, then open a new terminal window.

Every error you just fixed taught you how AI systems really behave. Imagine doing that with RAG, fine-tuning, and quantization under expert guidance. HCL GUVI’s Intel & IITM Pravartak Certified AI/ML Course lets you practise all of it through 20+ projects and 1:1 doubt sessions.

Conclusion

Being able to run Llama 4 locally says a lot about where artificial intelligence is heading. A model that understands both text and images can now run locally on your machine, work on your data, and stay under your control. As open models keep improving, this kind of ownership is likely to become a normal part of how people use AI.

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