Llama 4 Scout

Our Take

A workable 109B-parameter MoE language model from Meta. Pulls ahead on competition math (AIME 2026) (85/100), so reach for it when that's the dimension that matters.

Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.

Llama 4 Scout introduces a 10,000,000 token context window. This is a generational leap over Llama 3.1’s 128k window. This massive capacity enables native RAG-less (Retrieval-Augmented Generation) workflows where entire technical libraries, thousands of high-resolution images, or massive datasets can be loaded directly into the KV cache for immediate reasoning.

Unlike previous iterations that relied on adapter-based vision modules, Scout features native multimodality. The vision and text encoders are deeply integrated, allowing the model to reason across visual and textual data simultaneously. This is critical for tasks like architectural diagram analysis, OCR on complex forms, and spatial reasoning within video frames.

Llama 4 Scout is not a general-purpose "chat" model; it is a reasoning engine. Its training emphasizes instruction-following and logic, making it particularly effective for technical pipelines.

With its 10M context window, Llama 4 Scout for coding excels at repository-level understanding. You can feed the model an entire codebase to identify architectural bottlenecks, perform security audits, or refactor legacy code across hundreds of files. Its performance on Llama 4 Scout reasoning benchmarks indicates a significant improvement in logic-heavy tasks like debugging complex asynchronous logic in Rust or C++.

The native vision capabilities allow Scout to act as a sophisticated document processor. It can handle:

• Complex OCR: Extracting data from non-standard layouts, handwritten notes, and low-quality scans.
• Visual Reasoning: Interpreting charts, heatmaps, and technical blueprints to provide actionable summaries.
• Function Calling: Scout can identify UI elements in a screenshot and generate the corresponding code to interact with them via API or automation scripts.

The August 2024 training cutoff ensures the model is current with modern mathematical notation and diverse linguistic nuances. It supports over 30 languages with high proficiency, making it a viable choice for local translation and localization tasks that require high context retention.

To run Llama 4 Scout locally, you must distinguish between compute requirements and memory requirements. While the 17B active parameters make it fast, the full 109B parameters must reside in VRAM for the model to function without significant offloading penalties.

VRAM is the primary bottleneck for this model. Because it is a 109B parameter model, the memory footprint is substantial:

• FP16 (Unquantized): ~218 GB (Requires an enterprise cluster or 3x A100/H100 80GB).
• Q8_0 (8-bit): ~115 GB (Requires 2x A6000s or a Mac Studio with 128GB+ RAM).
• Q4_K_M (4-bit): ~68 GB (The "sweet spot" for high-end consumer and prosumer setups).
• Q3_K_L (3-bit): ~52 GB (Allows for some context headroom on 2x RTX 3090/4090 setups).

For most practitioners, the best GPU for Llama 4 Scout depends on your budget:

• The Professional Choice: A single NVIDIA H100 (80GB) or A6000 (48GB) x2. This allows for Q4 or Q5 quantization with enough overhead for the KV cache.
• The Prosumer Choice: 3x NVIDIA RTX 3090 or 4090 (24GB each). This 72GB VRAM total is ideal for running the model at 4-bit quantization (Q4_K_M).
• The Unified Memory Choice: Mac Studio with M2/M3/M4 Ultra and at least 128GB of Unified Memory. This is the most seamless way to run 109B model on consumer-adjacent hardware without the complexities of multi-GPU PCIe lane bottlenecks.

For daily use, the best quantization for Llama 4 Scout is Q4_K_M GGUF or 4.0bpw EXL2. At 4-bit, the model retains nearly all its reasoning capabilities while fitting into a ~68GB footprint. If you are prioritizing the 10M context window, you may need to drop to Q3_K_S to leave room for the massive KV cache, which grows linearly with context usage.

The quickest way to deploy is via Ollama. Once your hardware is configured, you can pull the model directly:

ollama run llama4-scout:109b-q4_K_M

When evaluating Llama 4 Scout hardware requirements and performance, it is helpful to compare it against its closest competitors in the 100B+ parameter range.

Mixtral 8x22B is the other major player in the open-weights MoE space.

• Context: Scout wins decisively with 10M tokens vs. Mixtral’s 64k.
• Modality: Scout has native vision; Mixtral 8x22B is text-only (unless using a vision adapter).
• Efficiency: Scout’s 17B active parameters make it feel snappier during inference compared to Mixtral’s 39B active parameters.

While Llama 3.1 70B is a dense model and easier to fit on 2x RTX 3090s (48GB VRAM), Scout offers a significant jump in intelligence.

• Reasoning: Scout performs better on multi-step logic and complex instruction following.
• Speed: Despite being a larger model (109B vs 70B), Scout’s Llama 4 Scout MoE efficiency allows it to match or exceed the tokens per second of the 70B dense model on equivalent hardware.
• VRAM: The 70B model is significantly more accessible for users with only 48GB of VRAM. If you cannot reach the ~68GB threshold for Scout, the 70B remains the better local choice.

Llama 4 Scout is the definitive local AI model 109B parameters 2025 choice for users who need a "big model" experience without the latency of a massive dense architecture. If you have the VRAM to support it, the combination of 10M context and native vision makes it one of the most versatile tools available for local deployment.