Llama 4 Scout & Maverick: Can You Run Meta's New AI on Your Mac?

What Changed in Llama 4

Llama 4 represents Meta's biggest architectural shift since the original Llama release. Both Scout and Maverick abandon the dense transformer design of Llama 3 in favor of Mixture-of-Experts (MoE) -- a sparse architecture where only a fraction of the model's total parameters activate for each token.

Both models are also natively multimodal. They process images and text in a single forward pass with no adapter needed. Meta trained them on a massive multilingual corpus, and both support the Llama 4 tokenizer with a 200K vocabulary.

The practical impact for Mac users: MoE means you can run a model with frontier-class knowledge while only needing enough RAM for the active parameters plus the routing overhead. This is what makes Scout feasible on consumer hardware.

Llama 4 Scout: The Mac-Friendly Model

Scout is the model Mac owners should care about. Here are the numbers that matter:

• Total parameters: 109B across 16 experts
• Active parameters per token: 17B
• Context window: 10 million tokens (industry-leading)
• Architecture: MoE with 16 experts, top-1 routing
• Modalities: Text + image input, text output
• License: Llama 4 Community License

The 10M token context window is the standout feature. No other locally-runnable model comes close. You can feed Scout an entire codebase, a full novel, or months of meeting transcripts in a single prompt. In practice, context this large requires significant memory overhead, so you will want 128GB to fully exploit it. On a 64GB machine, expect practical context limits around 128K-256K tokens.

Scout Performance on Apple Silicon

Llama 4 Maverick: Server-Only Beast

Maverick scales the MoE architecture dramatically:

• Total parameters: ~400B across 128 experts
• Active parameters per token: 17B (same as Scout)
• Experts: 128 (8x more than Scout)
• Memory requirement: ~200GB at Q4, ~400GB at FP16

The math does not work for any Mac currently on sale. Even the 192GB M4 Ultra Mac Studio cannot load a Q4 Maverick while leaving headroom for the OS and context. This is a model built for multi-GPU server racks -- think 4x H100 or 8x A100 configurations.

What Maverick proves is that MoE can scale expert count without increasing per-token compute. Both Scout and Maverick activate 17B parameters per token, but Maverick's 128-expert pool gives it access to far more specialized knowledge. It scores competitively with GPT-5 and Claude on major benchmarks while remaining open-weight.

MoE Architecture: Why It Matters for Your Mac

Traditional dense models activate every parameter for every token. A dense 70B model needs all 70B parameters in memory, and all 70B participate in each computation.

MoE models work differently. They contain a router network that selects which experts handle each token. Scout uses top-1 routing: for each token, only 1 of 16 experts activates alongside the shared attention layers. The result:

• Memory: You still need to load all experts into RAM (109B total), but the active compute per token is only 17B
• Speed: Inference is dramatically faster than a dense 109B model would be
• Quality: The model retains the knowledge of 109B parameters while running at 17B speed

This is why MoE is becoming the dominant architecture for local AI. You get more capability per gigabyte of RAM than any dense model can deliver. See our full MoE explainer for the technical deep-dive.

How to Install Llama 4 Scout on Mac

The fastest path is Ollama. Three commands and you are running:

# Install Ollama (if you haven't already)
curl -fsSL https://ollama.com/install.sh | sh

# Pull Llama 4 Scout (Q4 quantized, ~40GB download)
ollama pull llama4-scout

# Start chatting
ollama run llama4-scout

For the best performance on Apple Silicon, you can also run Scout through the MLX framework:

# Install MLX and the LM package
pip install mlx-lm

# Run Llama 4 Scout with MLX optimization
mlx_lm.generate --model mlx-community/Llama-4-Scout-Q4 --prompt "Explain MoE"

MLX typically delivers 20-50% higher throughput than llama.cpp on Apple Silicon because it is optimized specifically for the Metal GPU and Neural Engine. If you are on an M4 or M5 Mac, MLX is the way to go.

Want a full GUI experience? LM Studio also supports Llama 4 Scout with a drag-and-drop interface and built-in quantization options.

Scout vs Qwen 3.5 vs DeepSeek

How does Scout stack up against the other top models for Mac users?

• Llama 4 Scout (109B, 17B active): Best context window (10M), strong multimodal, needs 64GB. Best for: document-heavy workflows, image analysis, long-context tasks.
• Qwen 3.5 30B-A3B (30B, 3B active): Runs on 16GB Mac at ~58 tok/s. Far more accessible but smaller capability ceiling. Best for: everyday chat, coding on lower-RAM Macs.
• DeepSeek V3 (685B, 37B active): Server-only. Highest benchmark scores but not locally runnable. Best for: API access when you need maximum quality.

For Mac users with 64GB+, Scout is the new heavyweight champion of local AI. For 16-32GB Macs, Qwen 3.5 remains the better choice due to its tiny active parameter count. Check our live leaderboard for the latest benchmark comparisons.