Making a vintage LLM from scratch

Building LLMs Like It's 1995: What You Need to Know

A developer recently shared their experience creating a large language model from scratch using older, foundational techniques—eschewing modern frameworks and efficiency tricks to understand how these systems actually work at their core. The project, documented on a personal blog, generated discussion on Hacker News about the value of understanding fundamentals versus using contemporary tools.

This matters because the AI industry has moved so quickly that many practitioners work with pre-built models and high-level APIs without understanding the underlying mechanics. Rebuilding models with vintage approaches forces engagement with first-principles thinking about how neural networks learn language patterns.

TL;DR

• From-scratch implementation: Building an LLM without relying on optimized libraries reveals the actual mathematical operations that make language models function
• Educational value: Understanding classical approaches helps practitioners comprehend why modern optimizations matter and how they solve specific problems
• Impact: This knowledge deepens expertise and enables better debugging, fine-tuning, and architectural decisions when working with contemporary models

Background

Large language models have become commodity tools, available through API calls from OpenAI, Anthropic, Meta, and others. Most ML engineers interact with these systems at a high level—prompting them, fine-tuning them, or integrating them into applications. The complexity of modern implementations, with techniques like flash attention, mixed-precision training, and distributed computing across thousands of GPUs, can obscure what's happening mathematically.

This knowledge gap isn't unique to AI. Every generation of software engineers faces abstractions that hide implementation details. The difference with LLMs is their importance: they're reshaping how software works, yet their mechanics remain opaque to many who deploy them.

Building a model from scratch—using only fundamental linear algebra, basic Python, and perhaps PyTorch at its lowest levels—strips away optimizations and forces clarity. It's similar to why computer science programs still teach sorting algorithms despite modern languages providing optimized sort functions. The exercise builds intuition.

Earlier attempts at educational AI projects have taken similar approaches. Projects like Andrej Karpathy's micrograd or Nanograd demonstrate that implementing backpropagation from scratch teaches more than reading papers. Building a full LLM extends this principle across attention mechanisms, tokenization, embedding spaces, and training loops.

How it works

Understanding the Core Architecture

A vintage LLM implementation starts with the transformer architecture introduced in 2017's "Attention Is All You Need" paper. Rather than using a library that abstracts away layers, a from-scratch build manually implements each component.

The transformer stack contains multiple identical layers. Each layer has two main parts: a self-attention mechanism and a feed-forward network. The self-attention component allows the model to weigh the importance of different tokens when processing any given token. Building this manually involves creating query, key, and value matrices, computing similarity scores between every pair of tokens, and using these scores to create weighted combinations.

Without optimized libraries, this is computationally expensive, but the bottleneck becomes pedagogically useful—you understand exactly why attention is expensive and why techniques like multi-head attention (splitting the computation into parallel "heads") help. You see why optimizations like key-value caching matter for inference.

Training Mechanics

Training happens through backpropagation: computing how much each parameter should change to reduce the loss function. In a vintage implementation, this means manually computing gradients layer by layer, understanding how errors propagate backward through the network.

This exposes the challenges in training: vanishing or exploding gradients, where signals become too small or too large as they propagate through many layers. Modern frameworks handle this silently through careful initialization and normalization techniques. Building from scratch, you encounter these problems directly and must implement solutions like layer normalization—which normalizes each layer's outputs to have consistent scale and variance.

Tokenization and Embeddings

Before any neural computation, text must be converted to numbers. A vintage approach typically uses simpler tokenization (perhaps character-level or byte-pair encoding) and creates embedding matrices that map tokens to high-dimensional vectors. These embeddings are learned during training—initially random, they gradually become meaningful through gradient updates.

Understanding embeddings from scratch reveals why they work: the model discovers that tokens with similar meanings should have similar vectors, not because the system was explicitly told this, but because it's statistically useful for predicting the next token.

The Educational Payoff

Creating a vintage LLM teaches several things that black-box APIs cannot:

Why modern optimizations exist: Once you implement attention naively, you understand why flash attention matters. Once you train on CPU, you understand why distributed training across GPUs is necessary for practical models.

How to debug failures: If fine-tuning doesn't work, understanding the underlying mechanisms helps identify whether the problem is learning rate, batch size, architecture, or data quality.

Design intuition: Future work might require custom modifications. Understanding fundamentals makes these modifications possible rather than treating models as complete black boxes.

What happens next

This educational approach remains niche—most practitioners rightfully use optimized frameworks. However, as AI systems become more critical infrastructure, deeper understanding among more engineers seems valuable. Universities increasingly include from-scratch neural network implementations in curricula.

The broader pattern is clear: as tools become more powerful and more abstracted, understanding their foundations becomes simultaneously less necessary for immediate productivity and more important for mastery. For those investing seriously in AI expertise, a vintage implementation project remains an excellent investment in foundational knowledge. This article does not contain affiliate links.