Large Language Model (LLM) Architecture

Transformer Architecture: The Foundation of Modern LLMs

The Transformer architecture represents a fundamental breakthrough in natural language processing and forms the basis of all modern large language models (LLMs). Unlike previous approaches based on recurrent (RNN) or convolutional (CNN) neural networks, transformers utilize the so-called attention mechanism, which allows for efficiently capturing long-range dependencies in text without sequential processing. This architectural foundation is key for the effective process of training language models.

A key feature of the Transformer architecture is its parallelizability - all tokens of the input sequence can be processed simultaneously, which dramatically speeds up both training and inference. A standard transformer consists of an encoder and a decoder, with modern LLMs like GPT primarily using a decoder-only architecture, while models like BERT are encoder-only. Models like T5 or BART utilize the complete encoder-decoder architecture.

Technical Specifications of Transformer Models

Modern LLMs such as GPT-4, Claude, or Llama 2 implement deep transformer architectures with tens to hundreds of layers. Each layer processes information through multi-head attention mechanisms and feed-forward neural networks. The model's performance is largely determined by the number of parameters (weights), which ranges from units of billions for smaller models up to hundreds of billions or even trillions for the largest systems.

Self-Attention Mechanisms and Their Implementation

Self-attention (sometimes also called scaled dot-product attention) is a key component of the Transformer architecture. This mechanism allows the model to evaluate relationships and dependencies between all tokens in a sequence and dynamically determine which parts of the text to focus on when interpreting a specific word or phrase.

Technically, self-attention transforms each token into three different vectors: query (Q), key (K), and value (V). The subsequent attention calculation involves matrix multiplication of Q and K, scaling the result, applying the softmax function to obtain attention weights, and finally multiplying with the V matrix to get a contextually enriched representation. Mathematically, this process can be expressed by the equation:

Attention(Q, K, V) = softmax(QK^T / √d_k)V

Multi-Head Attention

Modern LLMs utilize multi-head attention, which allows the model to simultaneously track different types of relationships in the text. For example, one attention head might track syntactic relationships, while another focuses on semantic similarity or coreference relations. The number of attention heads is an important hyperparameter, typically ranging from 12 in smaller models up to 96 or more in the largest systems. Each head operates in a lower dimension than the original embedding vector, ensuring computational efficiency while maintaining the model's expressive power.

Embedding Dimensions and Token Representation

The embedding dimension is a key hyperparameter that determines the size of the vector representation of individual tokens in the language model. In modern LLMs, this value typically ranges from 768 for smaller models to 12288 or more for the largest systems. A larger embedding dimension allows capturing finer semantic nuances and more complex linguistic relationships, but at the same time increases the computational complexity and the number of model parameters.

The process of converting tokens to embeddings involves a lookup table where each possible token corresponds to a unique embedding vector. These initial embeddings are further enriched with positional information through positional embeddings, which can be implemented either as learnable parameters or using deterministic sinusoidal functions.

Contextual Capacity of Embeddings

An important aspect of embeddings in LLMs is their contextual capacity, i.e., the ability to retain information about relationships between tokens across long sequences. Modern models like GPT-4 or Claude 3 Opus achieve context windows of 32K to 128K tokens, enabling the processing of long documents, complex conversations, or sophisticated instructions. Proper implementation of positional embeddings is critical for effectively scaling the context window, with advanced models using techniques like RoPE (Rotary Position Embedding) or ALiBi (Attention with Linear Biases) to improve performance on long sequences.

Feed-Forward Neural Networks in LLMs

Feed-forward neural networks (FFNs) form the second main component of each transformer layer, following the self-attention mechanism. While attention captures relationships between tokens, FFNs process information for each token independently and apply non-linear transformations that are crucial for the model's expressive power.

A typical FFN implementation in a transformer involves two linear transformations with an activation function (most commonly ReLU or GELU) in between. Mathematically, this process can be expressed as:

FFN(x) = Linear₂(Activation(Linear₁(x)))

Parameterization and Optimization of FFNs

From an architectural perspective, the key parameter of the FFN is the hidden dimension, which determines the size of the intermediate result after the first linear transformation. This value is typically 4 times larger than the embedding dimension, ensuring sufficient capacity to capture complex patterns. In modern architectures like PaLM or Chinchilla, experiments are conducted with alternative configurations, including SwiGLU or GeGLU activations and mixtures-of-experts approaches, which further increase the efficiency of FFN components.

An interesting aspect of FFN components is that they constitute the majority of parameters in modern LLMs - typically 60-70% of all weights. This makes them primary candidates for optimization techniques such as pruning (removing unnecessary weights), quantization, or low-rank approximation in cases where reducing the model's memory requirements is necessary.

Quantization and Other Optimization Techniques

Quantization is a key optimization technique that allows reducing the memory requirements of LLMs while preserving most of their capabilities. The principle involves converting model parameters from high precision (typically 32-bit float values) to lower precision (16-bit, 8-bit, or even 4-bit representations). Properly implemented quantization can reduce the model size by up to 8x with minimal impact on the quality of responses.

Modern approaches like GPTQ, AWQ, or QLoRA implement sophisticated quantization algorithms that optimize the process based on the statistical properties of the weights and their importance for model accuracy. Post-training quantization (PTQ) applies compression to an already trained model, while quantization-aware training (QAT) integrates quantization aspects directly into the training process.

Other Optimization Techniques

In addition to quantization, modern LLMs utilize a range of other optimization techniques:

Model pruning - systematic removal of less important weights or entire model components based on their impact on the final performance

Knowledge distillation - training a smaller "student" model to mimic the behavior of a larger "teacher" model

Low-rank adaptation (LoRA) - modifying selected model components using low-rank matrices, which allows for efficient fine-tuning with minimal memory requirements

Sparse attention - implementation of attention mechanisms that do not need to evaluate relationships between all tokens, but focus only on potentially relevant pairs

Model Sharding and Distributed Processing

Model sharding is a technique for distributing the parameters and computations of large language models across multiple computing devices (GPUs/TPUs), enabling efficient training and deployment of models that are too large to fit into the memory of a single accelerator. There are four main approaches to sharding, each with its own advantages and limitations.

Tensor Parallelism splits individual matrices and tensors into segments that are processed simultaneously on different devices. This approach minimizes communication overhead but requires high-speed interconnection between accelerators.

Pipeline Parallelism distributes entire layers of the model across different devices, which process data sequentially like a pipeline. This approach utilizes memory efficiently but can lead to unbalanced device utilization.

Advanced Distribution Strategies

3D Parallelism combines tensor and pipeline parallelism with data parallelism (processing different batch samples on different devices), which allows for maximum utilization of available computing resources when training extremely large models.

ZeRO (Zero Redundancy Optimizer) eliminates redundancy in storing optimizer states, gradients, and model parameters across GPUs. ZeRO-3, the most advanced variant, partitions individual model parameters such that each GPU stores only a small fraction of the total model, enabling the training of multi-billion parameter models even on relatively limited hardware systems.

Implementing effective sharding strategies requires specialized frameworks like DeepSpeed, Megatron-LM, or Mesh TensorFlow, which automate the complex aspects of distribution and synchronization. These frameworks often implement further optimizations such as gradient checkpointing, mixed-precision training, or activation recomputation to further improve efficiency and reduce memory requirements.

Comparison of Modern Language Model Architectures

Architectural differences between modern LLMs play a key role in their capabilities, efficiency, and suitability for various applications. While all utilize the transformer foundation, significant variations exist in the implementation of individual components, affecting their performance and characteristics.

GPT architecture (Generative Pre-trained Transformer) uses a decoder-only approach with autoregressive text generation, making it ideal for generative tasks. Newer versions like GPT-4 implement advanced techniques both at the architectural level (larger context window, multi-modal inputs) and at the training level (RLHF, constitutional approaches).

PaLM architecture (Pathways Language Model) from Google introduced innovations like SwiGLU activations, multi-query attention, and scaled RoPE, which enabled more efficient scaling to hundreds of billions of parameters. Gemini, the successor to PaLM, further integrated multimodal capabilities directly into the model architecture.

Specialized Architectures and New Approaches

Mixtures of Experts (MoE) like Mixtral represent a hybrid approach where each token is processed only by a subset of specialized "expert" networks. This technique allows dramatically increasing the number of model parameters while maintaining similar computational cost during inference.

State-space models like Mamba represent a potential alternative to transformers, combining the advantages of recurrent and convolutional approaches with linear scalability concerning sequence length. These models are particularly promising for processing very long contexts (100K+ tokens).

When choosing an architecture for a specific application, one must consider the trade-offs between accuracy, computational efficiency, memory requirements, and specific capabilities such as long-term memory or multimodal processing. The latest research focuses on hybrid approaches combining the strengths of different architectures and techniques like retrieval-augmented generation, which extend model capabilities with explicit access to external knowledge.