Run Llama 4 Locally: All Platforms Install Guide 2026

Quick Summary: Meta's Llama 4 is the most downloaded open-source model family of 2026, featuring the innovative Scout and Maverick Mixture-of-Experts architectures. Whether you're on a gaming PC, a MacBook Pro, or a Linux workstation, this guide walks you through the complete installation process across all platforms — including Android and iOS mobile setup — using Ollama.

What Is Llama 4?

Llama 4 is Meta's fourth-generation open large language model, released in early 2026. It represents a significant architectural shift from previous Llama versions: instead of dense transformer models, Llama 4 uses a Mixture-of-Experts (MoE) architecture, where only a fraction of the model's total parameters are activated for each token. This design delivers the quality of a much larger model at a fraction of the inference cost.

Llama 4 comes in two main variants for local deployment: Scout and Maverick. Scout is the lighter model optimized for speed and efficiency on consumer hardware, while Maverick is the larger, more capable variant for users with high-end workstations. Both are released under the Llama 4 Community License, which allows free use for most applications — including commercial use for platforms under 700 million monthly active users.

On multimodal benchmarks, Llama 4 Scout outperforms Google Gemma 3 27B and Microsoft Phi-4 on nearly every task while requiring significantly less active compute. The model supports images as input in addition to text, making it one of the most versatile open-source models available in 2026.

MoE

Architecture

Visual

Multimodal

10M+

Downloads

Free

Community License

Scout vs Maverick: Which Should You Run?

Choosing between Scout and Maverick comes down to your hardware and use case:

Llama 4 Scout

Total Params:109B

Active Params:17B per token

Context:10M tokens

Min VRAM:8GB (quantized)

Best For:Daily use, speed

ollama run llama4:scout

Llama 4 Maverick

Total Params:400B

Active Params:17B per token

Context:1M tokens

Min VRAM:24GB (quantized)

Best For:Complex reasoning

ollama run llama4:maverick

Recommendation for Most Users

Start with Llama 4 Scout. Despite having only 17B active parameters, its 109B total parameter space (distributed across 16 experts) gives it knowledge depth rivaling dense 70B models. Scout also supports a stunning 10 million token context window — the longest of any locally runnable model in 2026. If you need more raw capability and have an RTX 3090 or better, upgrade to Maverick.

Hardware Requirements

Model	RAM (CPU Mode)	VRAM (GPU Mode)	Disk	Speed
Llama 4 Scout (Q4)	16GB	8GB	~24GB	Fast
Llama 4 Scout (Q8)	32GB	16GB	~48GB	Good
Llama 4 Maverick (Q4)	48GB	24GB	~80GB	Moderate
Llama 4 Maverick (FP16)	128GB+	Multi-GPU	~160GB	Server

Important Note on MoE Models

MoE models like Llama 4 require more disk and total memory than the active parameter count suggests, because all the expert weights must be stored even though only some activate per token. Q4 quantization is highly recommended to make Llama 4 Scout runnable on consumer hardware.

Install Ollama (All Platforms)

macOS

Download the official .dmg installer or use Homebrew. Apple Silicon (M1–M4) delivers outstanding Llama 4 Scout performance via Metal GPU acceleration.

brew install ollama
# Then start it:
ollama serve

Windows

Download OllamaSetup.exe from ollama.com. Run as Administrator. Supports NVIDIA CUDA and AMD ROCm GPUs out of the box.

# After install, open PowerShell:
ollama list
# Shows installed models

Linux

Single-command install. Auto-detects NVIDIA/AMD GPUs. Works on Ubuntu, Debian, Fedora, CentOS, and Arch Linux.

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

Download and Run Llama 4

Once Ollama is installed, pulling and running Llama 4 is straightforward. The first time you run a model, Ollama automatically downloads it to your local model store. Models are cached permanently until you delete them with ollama rm, so you only pay the download cost once:

# Pull Llama 4 Scout (recommended for most users):

ollama pull llama4:scout

# Pull Llama 4 Maverick (for high-end hardware):

ollama pull llama4:maverick

# Run and start chatting immediately:

ollama run llama4:scout

# Send a single prompt and get output:

ollama run llama4:scout "Summarize the key differences between MoE and dense transformers"

Pro Tip: If the download is slow or times out, connect to VPN07 first and retry. VPN07's 1000Mbps bandwidth is specifically optimized for reaching Ollama's CDN nodes. For the 24GB Scout model, expect under 25 minutes with VPN07 vs potentially hours on a throttled connection.

After the model downloads (which can take 10–40 minutes depending on your connection speed and hardware), it stays cached locally. Subsequent runs start instantly without re-downloading.

Using Llama 4's Multimodal Vision Capability

Llama 4 supports image inputs natively. Via the API, you can send images alongside text prompts for visual analysis, captioning, or chart interpretation. This requires Ollama version 0.6+ and llama4:scout:

# Via curl (image URL):
curl http://localhost:11434/api/generate -d \
'{"model":"llama4:scout","prompt":"Describe this image","images":["<base64_data>"]}'

Add Open WebUI for a Browser Interface

Open WebUI gives you a full ChatGPT-style interface that connects directly to your local Ollama instance. It supports conversation history, system prompts, file uploads, and image inputs for Llama 4's vision features.

docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

Open http://localhost:3000 in your browser, create an account (local only), and select Llama 4 Scout from the model dropdown. You can now upload images directly in the chat and take advantage of Llama 4's multimodal capabilities.

Run Llama 4 on Android

Llama 4 Scout's MoE design — only 17B active parameters — makes it more feasible on powerful Android devices than traditional dense models of similar quality. Here are your options:

MNN (Mobile Neural Network) — Best Performance

MNN is Alibaba's open-source mobile inference engine, optimized specifically for Android GPU acceleration. The MNN app (available on GitHub as MNN-LLM) supports Llama 4 quantized models. On devices with Snapdragon 8 Gen 2 or later with 12GB+ RAM, you can run the 4-bit quantized version of a smaller Llama 4 variant at around 5–8 tokens per second.

Remote Access via Ollama

If you have Ollama running on a home PC or Mac, the easiest Android option is to connect remotely. Enable remote access on your desktop (OLLAMA_HOST=0.0.0.0 ollama serve), then use the Enchanted app or AnythingLLM mobile app to connect to your desktop's IP address. This runs Llama 4 Scout at desktop speed while your phone just handles the UI.

Run Llama 4 on iPhone / iPad

Apple's Neural Engine in recent iPhone and iPad chips makes them surprisingly capable for local LLM inference. The iPad Pro M4 with 16GB RAM can run Llama 4 Scout quantized variants at impressive speeds.

Enchanted (Free — Connects to Mac Ollama)

Install Enchanted from the App Store. Set your Mac's Ollama to accept network connections, then point Enchanted at your Mac's IP address on the same Wi-Fi network. Select Llama 4 Scout and enjoy full multimodal capability from your iPhone — the Mac does the compute, your phone is the interface.

LM Studio (iOS — On-Device)

LM Studio's iOS version supports running quantized Llama 4 models on-device using Apple's MLX framework. On iPad Pro M4 with 16GB RAM, you can run Q4 quantized Llama 4 Scout at 6–10 tokens/second. Search the in-app model library for "llama4" to find compatible GGUF versions.

API Usage with Llama 4

Ollama's API is OpenAI-compatible, making it trivial to swap in Llama 4 for any application that uses GPT-4 or Claude:

# Python with OpenAI SDK:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")

chat = client.chat.completions.create(

model="llama4:scout",

messages=[{"role": "user", "content": "Explain quantum computing in 3 sentences"}]

)

print(chat.choices[0].message.content)

You can also send streaming requests for real-time token-by-token output, useful for building chat applications:

curl http://localhost:11434/api/chat -d \
'{
  "model": "llama4:scout",
  "messages": [{"role": "user", "content": "Write a Python web scraper"}],
  "stream": true
}'

Common Issues and Fixes

Problem: Slow download or connection timeout

Cause: Ollama CDN may be throttled or geo-blocked. Fix: Use VPN07 before downloading. With 1000Mbps bandwidth across 70+ countries, VPN07 connects you to the fastest CDN node available. The Llama 4 Scout model is ~24GB, so a good connection matters — with VPN07, it typically takes 15–25 minutes.

Problem: "model not found" error

Cause: Wrong tag name. Fix: Run ollama search llama4 to see all available tags. As of March 2026, the correct tags are llama4:scout and llama4:maverick.

Problem: Insufficient memory error

Cause: MoE models need more memory than their active parameter count suggests. Fix: For Scout, ensure at least 16GB system RAM and 8GB VRAM. If still failing, try the most quantized version: ollama run llama4:scout-q2_k (lowest quality but runs on 6GB VRAM).

Llama 4's Context Window: A Game Changer

One of Llama 4 Scout's most remarkable features is its 10 million token context window — the longest of any locally runnable model available in 2026. To put this in perspective: 10 million tokens is roughly 7,500 pages of text, or the entire content of multiple books loaded at once. This enables use cases that were previously only possible with expensive cloud APIs:

📚 Analyze an Entire Codebase

Load an entire software project — all source files, tests, and documentation — into context and ask questions about architecture, identify bugs, or generate documentation for the whole codebase at once.

📄 Process Long Documents

Feed a 1,000-page legal contract, scientific paper, or financial report into Llama 4 Scout and extract specific information, generate summaries, or compare sections — all without chunking or RAG complexity.

Note that utilizing the full 10M context window requires substantial RAM — plan on 128GB+ system memory for full-length contexts. For typical use cases, a 32K–128K context is sufficient and runs well on 16–32GB RAM.

Advanced: Build Applications with Llama 4

Beyond simple chat, Llama 4 Scout's combination of multimodal input and a massive 10M token context window enables powerful new application patterns that were previously only possible with expensive cloud APIs.

📊 Automated Report Analysis Pipeline

Build a system that ingests PDF reports (converted to text), tables, and embedded charts (as images). Llama 4 Scout can process all these modalities simultaneously in a single prompt — extracting key metrics, identifying trends, and generating executive summaries. With a 10M token context window, you can feed an entire year of financial reports in one request.

🔍 Full Codebase Code Review

Load an entire software repository into context — all source files, test suites, and documentation. Ask Llama 4 Scout to identify security vulnerabilities, suggest architectural improvements, or explain how a specific feature works across multiple files. This eliminates the need for complex RAG (Retrieval-Augmented Generation) pipelines for smaller to mid-size codebases.

🎨 Visual QA Automation

Send screenshots of your web application alongside test descriptions to Llama 4. The model can verify UI elements are correct, check for visual regressions, and flag unexpected changes — acting as an automated visual QA tester. Combine this with a Playwright or Selenium script that captures screenshots and feeds them to the Llama 4 API for continuous visual testing.

These patterns highlight why local LLM deployment has become a priority for privacy-conscious developers and enterprises in 2026. When you run Llama 4 Scout on your own hardware, sensitive codebases, confidential reports, and proprietary data never leave your infrastructure — no cloud provider receives or stores your prompts or outputs.

For production deployments, consider pairing Ollama with LiteLLM as a load balancer if you need to scale across multiple machines or provide a consistent API endpoint that switches between Llama 4 Scout and Maverick depending on task complexity and available resources. LiteLLM's fallback configuration lets you automatically switch to a smaller, faster model when Maverick would be overkill, saving inference time on simple requests.

Llama 4 Setup Checklist

Ollama installed and service is running

Llama 4 Scout or Maverick downloaded

GPU acceleration verified (fast generation)

API tested at localhost:11434

Image inputs tested (for multimodal use)

VPN07 ready for large model downloads

Llama 4 Scout Speed Benchmarks by Platform

Llama 4 Scout's MoE architecture (17B active parameters) means it can run faster than its 109B total parameter count suggests. Here's what to expect on common hardware:

Hardware	Speed (t/s)	Context Length	Quality	Best Use
Apple M2 Ultra 192GB	20–30	Up to 10M tokens	Q8 quality	Full capability
Mac Studio M4 Max 128GB	25–35	Up to 1M tokens	Q8 quality	Daily AI work
2x RTX 4090 (48GB)	18–25	128K default	Q4 quality	Development server
RTX 4090 24GB	8–14	64K max	Q2–Q3	Single GPU Q2
CPU only (128GB RAM)	1–3	32K practical	Q4 quality	Batch processing only

Llama 4 Ollama Command Quick Reference

Complete reference for running Llama 4 with Ollama on any platform:

# ── Installation ──────────────────────────────────────

brew install ollama # macOS

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

# ── Download Llama 4 ──────────────────────────────────

ollama pull llama4:scout # recommended (8GB+ VRAM)

ollama pull llama4:maverick # high-end (24GB+ VRAM)

# ── Run Llama 4 ───────────────────────────────────────

ollama run llama4:scout

ollama run llama4:scout "Explain MoE architecture simply"

ollama run llama4:scout --num-ctx 131072 # use 128K context

# ── API Call ──────────────────────────────────────────

curl http://localhost:11434/api/chat -d \

'{"model":"llama4:scout","stream":false,

"messages":[{"role":"user","content":"Hello"}]}'

# ── Management ─────────────────────────────────────────

ollama list # show downloaded models

ollama ps # currently running models

ollama rm llama4:scout # remove to free disk space

Frequently Asked Questions

Q: Is Llama 4 better than Llama 3.3-70B?

Yes, in most benchmarks. Llama 4 Scout outperforms Llama 3.3-70B on multimodal tasks (since it processes images), matches or exceeds it on text reasoning, and does so with fewer active parameters (17B vs 70B active compute) — meaning faster inference. Llama 4 Maverick is significantly more capable than anything in the Llama 3 family. For users who currently run Llama 3.3-70B, upgrading to Llama 4 Scout is highly recommended.

Q: Can I use Llama 4 commercially?

Yes, for most businesses. The Llama 4 Community License allows commercial use for organizations with fewer than 700 million monthly active users. This covers virtually all small and medium businesses and most large enterprises. Platforms at social network scale (Facebook, YouTube, etc.) would need a separate Meta commercial license. Review the complete license terms at llama.meta.com for your specific use case.

Q: How much RAM do I need for Llama 4 Scout?

For GPU-accelerated inference of Llama 4 Scout in Q4 quantization, you need approximately 8GB VRAM (for the model to load) plus 8-16GB system RAM. For CPU-only inference (much slower), you need 16-32GB system RAM. The best experience is on Apple Silicon Macs with 32GB+ unified memory, where Metal acceleration handles the model smoothly, or on gaming PCs with RTX 4080/4090 GPUs.

Q: Does Llama 4 support function calling?

Yes. Llama 4 supports function calling natively, making it suitable for agentic workflows where the model needs to interact with external APIs and tools. Through Ollama's API, you can pass a tools array in the same format as OpenAI's function calling specification. Llama 4 Scout and Maverick both reliably extract correct function arguments and return properly structured JSON responses for tool calls.

Q: Will downloading Llama 4 be slow without a VPN?

It depends on your location and ISP. Llama 4 Scout is approximately 24GB in Q4 format — at 10Mbps download speed, that's over 5 hours. At VPN07's 1000Mbps, it's under 25 minutes. Users in regions with throttled access to Ollama's CDN or HuggingFace report download speeds 10–50x faster with VPN07 compared to their direct connection. For large model downloads, VPN07 essentially pays for itself in saved time within the first download.

📥 Download Llama 4 & all top LLMs: One-click Ollama commands, hardware guides & install tutorials in one place. View Hub →

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