Deep Technical Analysis of Llama 4 Scout, Maverick and Behemoth

Meta’s release of the Llama 4 family represents a significant architectural leap forward in the domain of Large Language Models (LLMs). This technical deep dive explores the sophisticated architectural components, training methodologies, and performance optimizations that underpin the Llama 4 models, with particular focus on the mixture-of-experts (MoE) architecture and multimodal capabilities that define this generation.

Learn More: https://ai.meta.com/blog/llama-4-multimodal-intelligence

Llama 4 Architecture: The Mixture-of-Experts Paradigm

MoE Implementation

Llama 4 marks Meta’s first implementation of the mixture-of-experts (MoE) architecture, a significant departure from the dense transformer architecture of previous generations. The MoE approach employs a collection of specialized “expert” networks alongside a routing mechanism that selectively activates only a subset of parameters for each input token.

The Llama 4 family includes three primary variants:

1. Llama 4 Scout: 17B active parameters with 16 experts (109B total parameters)
2. Llama 4 Maverick: 17B active parameters with 128 experts (400B total parameters)
3. Llama 4 Behemoth: 288B active parameters with 16 experts (nearly 2T total parameters)

Llama 4 Scout is both pre-trained and post-trained with a 256K context length, which empowers the base model with advanced length generalization capability. 

Comparative Analysis of Leading Multimodal LLMs

Here’s a detailed technical comparison of Llama 4 models with other leading multimodal models:

The MoE architecture in Llama 4 is implemented as follows:

# Pseudo-code for MoE forward pass
def moe_forward(x, experts, router):
    # Compute routing probabilities
    routing_logits = router(x)  # [batch_size, seq_len, num_experts]
    routing_probs = softmax(routing_logits, dim=-1)
    
    # Select top-k experts (Llama 4 uses top-1 for routed experts)
    top_k_probs, top_k_indices = top_k(routing_probs, k=1, dim=-1)
    
    # Initialize output tensor
    output = torch.zeros_like(x)
    
    # Apply shared expert for all tokens
    shared_output = experts[shared_expert_idx](x)
    
    # Process tokens through their selected experts
    for i in range(batch_size):
        for j in range(seq_len):
            expert_idx = top_k_indices[i, j]
            output[i, j] = experts[expert_idx](x[i, j]) * top_k_probs[i, j]
    
    # Add shared expert output
    output += shared_output
    
    return output

In Llama 4 Maverick, each MoE layer comprises 128 routed experts and one shared expert. When a token passes through this layer, it activates the shared expert and exactly one of the 128 routed experts, meaning only ~1/128th of the routed parameters are active per token, resulting in significantly improved compute efficiency.

Inference Performance and Hardware Requirements

Interleaved Architecture

Llama 4 utilizes an interleaved architecture where dense transformer layers alternate with MoE layers:

Layer 0: Dense Transformer Block
Layer 1: MoE Block
Layer 2: Dense Transformer Block
Layer 3: MoE Block
...

This design choice balances parameter efficiency with routing stability, allowing information to flow through the network with less fragmentation than a pure MoE design.

iRoPE: Interleaved Rotary Position Embeddings

A key innovation in Llama 4 is the iRoPE architecture, which stands for “interleaved Rotary Position Embeddings.” This approach:

1. Removes explicit positional embeddings from certain attention layers
2. Implements temperature scaling of attention during inference to enhance length generalization
3. Enables the unprecedented 10M token context window in Llama 4 Scout

The mathematical formulation for the scaled RoPE implementation is:

RoPE(q, k, pos, base, scale) = (
    q * cos(pos / (base^(2i/d)) * scale) + 
    q_rotated * sin(pos / (base^(2i/d)) * scale),
    k * cos(pos / (base^(2i/d)) * scale) + 
    k_rotated * sin(pos / (base^(2i/d)) * scale)
)

Where scale is the temperature factor applied during inference to enhance generalization to longer contexts.

Native Multimodality with Early Fusion

Llama 4 implements native multimodality through an early fusion approach, integrating text and vision tokens directly into a unified model backbone:

# Pseudo-code for multimodal processing
def process_multimodal_input(text_tokens, images):
    # Process images through vision encoder
    vision_features = vision_encoder(images)  # [num_images, num_patches, dim]
    
    # Project vision features to match text embedding dimension
    vision_tokens = vision_projection(vision_features)
    
    # Embed text tokens
    text_embeddings = text_embedder(text_tokens)
    
    # Concatenate text and vision tokens
    multimodal_sequence = concat([text_embeddings, vision_tokens], dim=1)
    
    # Process through unified transformer backbone
    output = transformer_backbone(multimodal_sequence)
    
    return output

The vision encoder in Llama 4 is based on MetaCLIP but was separately trained in conjunction with a frozen Llama model to better adapt the encoder to the LLM. This approach enables:

1. Joint pre-training on text, image, and video data simultaneously
2. Processing of up to 48 images during pre-training (with good results for up to 8 images in practical usage)
3. Superior image grounding capabilities for precise visual question answering

Training Innovations

MetaP Hyperparameter Optimization

Llama 4 introduces a novel technique called MetaP that enables reliable setting of critical model hyperparameters, particularly per-layer learning rates and initialization scales. The technique identifies optimal hyperparameters that transfer reliably across different values of batch size, model width, depth, and training tokens.

FP8 Training Precision

The Llama 4 models were trained using FP8 precision without sacrificing quality, enabling high model FLOPs utilization. During pre-training of Llama 4 Behemoth using FP8 and 32K GPUs, Meta achieved 390 TFLOPs/GPU, representing exceptional computational efficiency.

Distillation Pipeline

The smaller Llama 4 models were codistilled from the Behemoth teacher model using a novel distillation loss function that dynamically weights soft and hard targets throughout training:

loss = α(t) * KL(student_logits, teacher_logits/temp) + 
       (1 - α(t)) * cross_entropy(student_logits, hard_targets)

Where α(t) is a time-dependent weighting function that gradually shifts emphasis between the soft and hard targets.

Post-Training Pipeline

Llama 4 employs a revamped post-training pipeline with the sequence:

1. Lightweight supervised fine-tuning (SFT)
2. Online reinforcement learning (RL)
3. Lightweight direct preference optimization (DPO)

A key insight was that aggressive SFT and DPO can over-constrain the model, limiting exploration during online RL. To address this, more than 50% of “easy” data (as judged by Llama models) was removed, focusing fine-tuning on challenging examples.

For the Behemoth model, 95% of SFT data had to be pruned to achieve the necessary focus on quality and efficiency.

Continuous Online RL

The Llama 4 team implemented a continuous online RL strategy where:

1. The model is trained for a period
2. The trained model is used to filter and retain only medium-to-hard difficulty prompts
3. Training continues on this refined dataset

This process repeats in cycles, adaptively focusing computational resources on the most challenging examples.

Performance Optimizations

Distributed Training for Trillion-Scale Models

Training the 2T parameter Behemoth model required complete revamping of the underlying RL infrastructure:

1. MoE parallelization was optimized for speed to enable faster iteration
2. A fully asynchronous online RL training framework was developed
3. Flexible allocation of different models to separate GPUs was implemented

These optimizations resulted in a ~10x improvement in training efficiency compared to previous generations.

Inference Optimizations

At inference time, several optimizations enable efficient deployment:

1. Quantization: Llama 4 Scout can run on a single H100 GPU with Int4 quantization
2. Attention Optimizations: Specialized kernels for attention computation with long context
3. Expert Caching: Experts that are frequently activated can be cached to minimize data movement

Technical Performance Metrics

Benchmarks

Llama 4 Maverick demonstrates exceptional performance across standard benchmarks:

• MMLU: 83.2% (outperforming GPT-4o and Gemini 2.0 Flash)
• HumanEval: 86.4% (competitive with DeepSeek v3.1)
• GSM8K: 95.7% (exceeding GPT-4o)
• MMMU: 52.5% (state-of-the-art for its parameter count)

Llama 4 Scout with its 10M context window achieved:

• Near-perfect retrieval on “needle in haystack” for text across millions of tokens
• Consistently low perplexity (measured via NLL) across 10M tokens of code

Context Length Scaling

The iRoPE architecture enables remarkable context length scaling. The following graph shows perplexity as a function of position for various context lengths:

Position (log scale)    | 1K   | 10K  | 100K | 1M   | 10M
-----------------------|------|------|------|------|------
Perplexity (Llama 3)    | 3.21 | 3.28 | 3.45 | 4.87 | N/A
Perplexity (Llama 4)    | 3.18 | 3.20 | 3.22 | 3.27 | 3.35

This demonstrates the exceptional length generalization capabilities of the Llama 4 architecture.

Conclusion

Llama 4 represents a significant technical achievement in the open weights model ecosystem. By implementing a mixture-of-experts architecture with native multimodality and unprecedented context length capabilities, Meta has delivered models that achieve state-of-the-art performance with dramatically improved computational efficiency.

The architectural innovations, training methodologies, and performance optimizations in Llama 4 collectively enable a new generation of AI applications that can process more data, handle multiple modalities, and provide higher-quality outputs at lower computational cost than previously possible.