What I Learned From Implementing LLM Architectures From Scratch (And How to Get Started)

2026-05-12 · Source: Sebastian Raschka · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

This talk defines what it means to run or train Large Language Models (LLMs) in Python, emphasizing that it primarily involves using PyTorch, which acts as a Python interface to faster C++ or CUDA implementations. The presentation highlights the value of understanding LLM architectures by coding them from scratch, revealing nuances often omitted in papers. It details a personal process for dissecting new model releases, starting from technical reports and model cards, then analyzing `config.json` files and the actual PyTorch code to identify architectural differences and debug implementations. A significant portion focuses on current LLM architecture trends, particularly strategies for reducing KV cache size, such as Grouped Query Attention (GQA), Multi Latent Attention (MLA), Sliding Window Attention, Deepseek Sparse Attention, replacing attention layers with Mamba 2, and quantizing the KV cache. The discussion also touches on the evolution from conventional LLMs to reasoning models and agentic harnesses, and concludes with getting started tips for different proficiency levels, recommending open-source projects and libraries like Hugging Face Transformers.

Key takeaway

For AI Engineers and ML practitioners seeking to optimize LLM deployment, understanding the architectural nuances and KV cache reduction strategies is crucial. Your choice of attention mechanism (e.g., GQA, MLA, Sliding Window) directly impacts memory footprint and inference cost, especially with longer contexts. Consider implementing architectures from scratch to gain deep insights into these optimizations, which can then inform your selection and fine-tuning of models from platforms like Hugging Face for specific performance goals.

Key insights

Coding LLM architectures from scratch reveals critical design nuances and optimization strategies for memory and performance.

Principles

• PyTorch serves as a Pythonic "glue" to high-performance C++/CUDA.
• KV cache reduction is paramount for longer context LLM inference.
• Code provides verifiable architectural details often missing in papers.

Method

Analyze model `config.json` and PyTorch code, compare against reference implementations layer-by-layer, and debug deviations to understand architectural specifics and optimization choices.

In practice

• Implement LLMs from scratch to grasp underlying mechanics.
• Explore GQA, MLA, and Sliding Window Attention for KV cache reduction.
• Utilize Hugging Face Transformers for robust LLM development.

Topics

• LLM Architectures
• PyTorch Development
• KV Cache Reduction
• Grouped Query Attention
• Sliding Window Attention

Best for: Machine Learning Engineer, AI Engineer, AI Student

Related on AIssential

• Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention — LLM architectures are evolving with complex, targeted tweaks to optimize long-context efficiency and reduce computational overhead.
• Shipping LLMs (Part 2/6): What’s Actually in Your KV Cache? — The KV cache stores per-token key/value tensors, significantly impacting LLM memory consumption and context window cost.
• Fast KVzip: Much Faster KV Cache Eviction for Cheaper Inference — Causal attention in decoder-only LLMs limits context utilization when options precede context.
• Nvidia’s new technique cuts LLM reasoning costs by 8x without losing accuracy — Dynamic Memory Sparsification reduces LLM memory costs by intelligently compressing the KV cache without accuracy loss.
• A Visual Tour of Modern LLM Architectures — The LLM Architecture Gallery offers a visual, comparative resource for understanding diverse LLM designs and their efficiency trade-offs.
• Towards Multi-Model LLM Schedulers: Empirical Insights into Offloading and Preemption — Multi-model LLM schedulers must account for non-linear offloading sensitivity and model reload-dominated preemption costs.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Sebastian Raschka.