Introduction: Beyond the Black Box
In July 2026, a detailed technical analysis published on Habr (a prominent Russian tech blog) offered an unprecedented look into the inner workings of transformer models—the architecture that underpins virtually every major large language model (LLM) from GPT-4 to Gemini and Claude. The article, titled "Препарация трансформера" (Transformer Dissection), systematically deconstructs the transformer into its core components, revealing how attention mechanisms, feed-forward networks, and positional encodings interact to produce coherent, context-aware text. This is not another high-level overview; it is a surgical examination of the mathematical and engineering decisions that make transformers both powerful and computationally expensive.
For data scientists and ML engineers, understanding these internals is no longer optional. As models grow to hundreds of billions of parameters—Meta's LLaMA 4 reportedly reaches 400B parameters, and OpenAI's GPT-5 is speculated to exceed 1 trillion—the cost of inference and training has become prohibitive. The Habr article breaks down where this cost lives: in the multi-head attention layers, where each head computes scaled dot-product attention over sequences that can span 128,000 tokens or more. The authors provide concrete numbers: a single forward pass through a 70B-parameter transformer with 96 layers and 64 attention heads consumes approximately 1.4 petaflops of compute for a 4,096-token input. This is why companies like Google and Microsoft have invested billions in custom TPU and GPU clusters.
The article also highlights a critical insight: the transformer's success is not solely due to its architecture but also to the training regime. The authors describe how modern transformers use mixture-of-experts (MoE) layers to activate only a fraction of parameters per token, reducing effective FLOPs by 40-60% while maintaining model quality. This is a practical optimization that has become standard in models like Mixtral 8x7B and GPT-4. By understanding the dissection presented in this news, engineers can make informed decisions about model selection, fine-tuning, and deployment.
The Core Components of a Transformer
Multi-Head Self-Attention: The Heart of Context
The transformer's defining feature is its multi-head self-attention mechanism. The Habr article dissects this into three matrices—Query (Q), Key (K), and Value (V)—each of dimension d_k. For a sequence of length n, the attention score matrix is computed as softmax(QK^T / sqrt(d_k)). The authors note that for n=128,000 tokens, this matrix has 16 billion elements, requiring 128 GB of memory just for the attention scores. This is why modern implementations use flash attention and kernel fusion to reduce memory bandwidth bottlenecks.
Each attention head captures different relationships: one head might focus on subject-verb agreement, another on long-range dependencies like coreference resolution. The article provides an example from BERT-base (12 layers, 12 heads): the 5th head in layer 8 consistently attends to the second word of a noun phrase, while the 9th head tracks negation scope. This specialization emerges without explicit supervision, a result of the training objective (masked language modeling or autoregressive prediction).
Feed-Forward Networks: The Knowledge Store
After attention, each token passes through a two-layer feed-forward network (FFN) with a ReLU or GELU activation. The Habr article reveals that FFNs account for roughly two-thirds of the model's parameters. In GPT-3 (175B parameters), the FFNs alone contain ~117B parameters. The authors argue that these layers act as a key-value memory: the first layer expands the embedding dimension by a factor of 4 (from 12,288 to 49,152 in GPT-3), and the second layer compresses it back. This expansion allows the model to store factual knowledge—for example, the FFN in layer 24 of GPT-3 encodes the fact "Paris is the capital of France" as a distributed pattern across multiple neurons.
Positional Encoding: Why Order Matters
Unlike recurrent networks, transformers have no inherent notion of token order. The article explains how positional encodings—sinusoidal functions in the original paper, or learned embeddings in modern variants like RoPE (Rotary Position Embedding)—inject positional information. RoPE, used in LLaMA and GPT-4, rotates the Q and K vectors by an angle proportional to the token position, allowing the model to attend to relative distances rather than absolute positions. The authors provide a mathematical derivation: for position p, the rotation matrix R(p) has block-diagonal form with 2x2 rotation matrices, each parameterized by angle θ_i * p, where θ_i is a frequency that decreases with dimension i.
Training Dynamics: From Pretraining to Fine-Tuning
Pretraining: The Data Hunger
The Habr article emphasizes that pretraining a transformer requires enormous datasets—typically trillions of tokens. For example, the LLaMA 2 model (70B parameters) was trained on 2 trillion tokens from publicly available sources. The authors describe the training pipeline: data deduplication using MinHash, filtering for quality using a classifier trained on human-curated examples, and tokenization using SentencePiece or BPE with a vocabulary of 32,000-100,000 tokens. The compute cost is staggering: training a 70B model on 2 trillion tokens requires ~1,000 A100 GPUs running for 30 days, consuming 3.6 GWh of electricity—equivalent to the annual consumption of 300 US households.
Fine-Tuning: Adapting to Domain
After pretraining, transformers are fine-tuned for specific tasks. The article discusses parameter-efficient fine-tuning (PEFT) methods like LoRA (Low-Rank Adaptation), which adds trainable rank-decomposition matrices to attention layers while freezing the original weights. LoRA reduces trainable parameters from 175B to just 10M—a 17,500x reduction. The authors present a case study: fine-tuning a 7B model for medical question answering with LoRA achieved 92% accuracy on MedQA, compared to 93% with full fine-tuning, but used 98% less GPU memory.
Inference Optimization: Making Transformers Practical
Quantization and Pruning
Deploying transformers in production requires aggressive optimization. The Habr article covers post-training quantization (PTQ) using INT8 or INT4 precision. For example, quantizing GPT-3 from FP16 to INT8 reduces memory from 350 GB to 175 GB with only a 0.5% drop in perplexity on WikiText-103. The authors also discuss pruning: removing attention heads that contribute less than 1% of the total attention entropy. In BERT-base, pruning 20% of heads reduced inference latency by 15% with no measurable accuracy loss on GLUE benchmarks.
Speculative Decoding
A key optimization for autoregressive generation is speculative decoding, where a small draft model (e.g., 1B parameters) generates multiple tokens in parallel, and the large target model (e.g., 70B) verifies them. The article reports that this technique achieves 2-3x speedup on standard hardware. For instance, on an A100, generating 100 tokens with GPT-3 takes 2.8 seconds without speculative decoding versus 1.1 seconds with it.
The Future: Sparse Attention and Beyond
The Habr article concludes by examining emerging architectures that challenge the transformer's dominance. Sparse attention methods—like the BigBird and Longformer models—replace full attention with a combination of local windows and global tokens, reducing the O(n^2) memory complexity to O(n log n). For a 512,000-token sequence, this reduces attention memory from 1 TB to 2 GB. The authors also mention State Space Models (SSMs) like Mamba, which achieve linear complexity by replacing attention with a structured state space. Early benchmarks show Mamba-3B matching GPT-3-3B on language modeling perplexity while being 5x faster during inference.
Conclusion: The Transformer's Legacy
The transformer architecture, first proposed in 2017's "Attention Is All You Need," has proven remarkably adaptable to everything from text to images (Vision Transformers) to protein folding (AlphaFold). The Habr article's dissection reveals that its power lies in the elegant combination of attention for context, FFNs for knowledge, and positional encodings for order. As of 2026, the transformer remains the backbone of AI, but its dominance is being challenged by more efficient alternatives. For practitioners, understanding the anatomy of transformers—as laid out in this detailed analysis—is essential for building, deploying, and optimizing the next generation of intelligent systems.
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