Inside LLaMA 4: A Look at Maverick and Scout Models

 

Have you ever considered running a language model with 10 million tokens in one context? Or one that natively reads text and images without fuss? Meta's latest model release, LLaMA 4, provides that. It is not just another AI drop, guys; it is a huge deal that changes everything. 

I am going to talk about the two main players, Maverick and Scout, in this post. AI tinkerers, researchers, and developers looking to ship quicker may find something here. Let's d ive in. 

 

The LLaMA 4 Magic: What's New?

LLaMA 4 leads, not follows. Meta added a Mixture of Experts (MoE) architecture to both types. MoE has been there, but this implementation is sleek. Scout is slim with 16 experts and 109 billion parameters, whereas Maverick is powerful with 128 experts and 400 billion parameters. Both activate 17 billion parameters at once. 

That means now you got access to better models that do not cost as much. Additionally, they are inherently multimodal and can interpret both text and images. You no longer need to play around with different image processing. It is flawless. 

Meet Maverick & Scout: Two Brains, Two Missions

Let me explain what makes these two different. 

Maverick delivers uncompromising performance. The context window may reach 1M tokens when fine-tuned, FP8 support, and multi-GPU inference make it resemble a heavy-lifting workstation. 

Scout, however, is like a powerful laptop that does more than it seems. It generates high-quality output on a single server-grade GPU with int4 or int8 quantization. Both of these beasts are just amazingly excellent and user friendly. 

Moreover, both are instruction-tuned, so you can use them as chat assistants or as raw base models in case you're working with complex use cases. 

Getting Started with LLaMA 4 in Code

Here comes the fun section; coding. 

I tried basic text generation using the Maverick model. This one requires the latest transformers library and a powerful GPU setup, particularly for scaling. This is the setup: 

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "meta-llama/Llama-4-Maverick-17B-128E-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")

prompt = "What are the benefits of using Mixture of Experts models?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

This small code provides an informative, natural response straight out of the box. 

Going Multimodal: Text + Images in One Prompt 

LLaMA 4 shines here. My multimodal prompt requested the model to describe and compare two images. Like humans, it analyzed the images and replied with relevant text. 

Here's the code I used for that:

from transformers import AutoProcessor, Llama4ForConditionalGeneration

model_id = "meta-llama/Llama-4-Maverick-17B-128E-Instruct"
processor = AutoProcessor.from_pretrained(model_id)
model = Llama4ForConditionalGeneration.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)

messages = [
    {
       "role": "user",
       "content": [
           {"type": "image", "url": "URL_1"},
           {"type": "image", "url": "URL_2"},
           {"type": "text", "text": "Compare these two images."}
        ]
    }
]

inputs = processor.apply_chat_template(messages, return_tensors="pt", tokenize=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.batch_decode(outputs, skip_special_tokens=True))

This requires multi-GPU hardware and is best for production or intense R&D. It is amazing when it works. 

Deploying at Scale 

The good news is that these models are ready for real-world app deployment. 

LLaMA 4 interacts with Hugging Face's inference stack via TGI. Scout's int4 quantization works on smaller hardware, whereas Maverick's FP8 weights suit modern AI accelerators. 

The internal Xet storage speeds up downloads and minimizes space utilization via deduplication, which is useful if you manage several models. 

Performance: Do the Numbers Match the Hype?

They do. Maverick ranks near the top of open models with 80.5% on MMLU Pro and 69.8% on GPQA Diamond. Scout is close behind with 74.3% on MMLU and 57.2% on GPQA. Both models exceed prior LLaMA versions and compete with proprietary giants. 

Final Thoughts 

LLaMA 4 goes beyond being larger and badder. It is unique and smarter. Maverick and Scout are useful models for AI copilots, research assistants, and content development pipelines. 

So, what are you waiting for guys, try them out and tell me what you build. I will try additional multimodal prompts here and possibly be impressed again.