Transformers

Introduction: The Scaling Dilemma Traditional transformer models face a fundamental trade-off: to increase model capacity, you must scale all parameters proportionally. If you want a smarter model, every single token must pass through every single parameter. This is dense activation, and it’s extremely expensive. Enter Mixture-of-Experts (MoE): a revolutionary architecture that achieves massive model capacity while keeping computational costs manageable through sparse activation. Models like GPT-4, Mixtral, and Switch Transformer use MoE to reach trillion-parameter scales while using only a fraction of those parameters per token. ...

Introduction: Words as Numbers How do language models understand meaning? The answer lies in embeddings: representing words, sentences, and entire documents as vectors of numbers in high-dimensional space. In this space: Similar words cluster together Analogies emerge as geometric relationships Meaning becomes computable through vector arithmetic Let’s visualize this invisible geometry where meaning is distance. From Words to Vectors Traditional Approach: One-Hot Encoding graph TB A[Vocabulary:cat, dog, king, queen, apple] A --> B[cat = 1,0,0,0,0] A --> C[dog = 0,1,0,0,0] A --> D[king = 0,0,1,0,0] A --> E[queen = 0,0,0,1,0] A --> F[apple = 0,0,0,0,1] style B fill:#e74c3c style C fill:#e74c3c style D fill:#e74c3c style E fill:#e74c3c style F fill:#e74c3c Problem: No semantic relationship! ...

Introduction: The Memory Wall Modern LLMs can process context windows of 100K+ tokens. But there’s a hidden cost: the KV cache. As context grows, the memory required to store key-value pairs in attention explodes quadratically. This creates a bottleneck: Memory: KV cache can consume 10-100× more memory than model weights Bandwidth: Moving KV cache data becomes the primary latency source Cost: Serving long-context models requires expensive high-memory GPUs Two innovations address this: Grouped Query Attention (GQA) and Multi-Head Latent Attention (MLA). They reduce KV cache size by 4-8× while maintaining quality. ...

Introduction: The Quadratic Bottleneck Transformers revolutionized AI, but they have a fundamental flaw: quadratic scaling. Processing a sequence of length n requires O(n²) operations due to self-attention. Every token attends to every other token, creating an all-to-all comparison: Context length: 1K 10K 100K 1M Operations: 1M 100M 10B 1T Time (relative): 1× 100× 10,000× 1,000,000× This makes long-context processing prohibitively expensive. Enter State Space Models (SSMs), specifically Mamba: a new architecture that processes sequences in linear time O(n) while maintaining long-range dependencies. ...

Introduction: The Paper That Changed Everything In 2017, Google researchers published “Attention is All You Need”, introducing the Transformer architecture. This single paper: Eliminated recurrence in sequence modeling Introduced pure attention mechanisms Enabled massive parallelization Became the foundation for GPT, BERT, and all modern LLMs Let’s visualize and demystify this revolutionary architecture, piece by piece. The Problem: Sequential Processing is Slow Before Transformers: RNNs and LSTMs graph LR A[Word 1The] --> B[Hidden h1] B --> C[Word 2cat] C --> D[Hidden h2] D --> E[Word 3sat] E --> F[Hidden h3] style B fill:#e74c3c style D fill:#e74c3c style F fill:#e74c3c Problem: Sequential processing—each step depends on the previous. Can’t parallelize! ...