Training GPT-2 From Scratch on a GTX1050

Last Updated on June 14, 2026 by Editorial Team Author(s): malrsapps Originally published on Towards AI. Training GPT-2 From Scratch on a GTX1050 This project started with curiosity rather than a clear plan. I wanted to understand how language models were actually trained and whether it was possible to build and train one myself on local hardware. At first, I considered building a small coding assistant. Later, I shifted toward a Turkish tax-focused model. Along the way, I found myself digging into datasets, tokenizers, training loops, GPU limitations, and model architecture. What began as a simple learning project eventually turned into training GPT-2 from scratch on a GTX1050. One of the local training runs running at full utilization on a GTX1050-class GPU with only 4GB VRAM. At the time, I only had access to a GTX1050-class GPU, limited VRAM, and a local Linux setup. I knew the hardware was far from ideal, but I wanted to understand how much experimentation was still possible under constrained conditions. Long-running experiments also introduced practical concerns such as heat, system stability, and power interruption risks during multi-day training runs. What started as a small experiment eventually evolved into tokenizer experiments, chained training workflows, runtime recovery systems, schedule-driven orchestration, and a full low-resource experimentation toolkit. Most importantly, it changed the way I think about training workflows on limited hardware. The Original Goal Was Completely Different My first idea was not related to Turkish language models at all. Initially, I wanted to train a coding-oriented model locally. I started downloading large open-source code datasets — some of them tens of gigabytes in size — and attempted to run training experiments directly on my local machine. That failed almost immediately. The datasets were too large, memory usage became unstable, and the overall workflow simply did not fit the hardware constraints I was working with. At that stage, I realized the first bottleneck was not the model itself. The real bottleneck was building a workflow that could survive on limited hardware. After those failed coding-model experiments, I changed direction. Instead of a coding model, I decided to experiment with a Turkish tax-oriented assistant. Since I work in accounting and tax-related fields, I already had structured domain knowledge and legal text sources available. I prepared an initial dataset using Turkish tax law documents and attempted a small fine-tuning experiment on GPT2-style models. The results were terrible. The problem quickly became obvious: The base model did not actually understand Turkish well enough. At first, I assumed I simply needed a better pretrained Turkish model. I searched Hugging Face for Turkish GPT-style models, downloaded several alternatives, and tried multiple fine-tuning runs. The outputs were still weak. Some models generated broken Turkish, some produced unstable outputs, and some simply failed to generalize in useful ways. That period taught me an important lesson: Not every published pretrained model is necessarily well-trained or practically usable. At that point, the project direction changed again. Instead of trying to fine-tune weak Turkish models, I decided to focus on a different problem entirely: First, I needed to build a GPT-style model that could actually learn Turkish reasonably well on constrained hardware. Discovering Wikipedia Training Once I realized the main issue was not fine-tuning but the lack of a strong Turkish base model, my focus shifted completely. The new goal became much simpler in theory: Train a small GPT-style model that could at least learn Turkish properly. Since GPT2-sized architectures were the only realistic option for my hardware, I started exploring how small-scale Turkish pretraining could work locally. That search eventually led me to Wikipedia datasets. At the time, Wikipedia looked manageable compared to the massive coding datasets I had attempted earlier. But even then, almost every stage introduced new problems. Tokenizer training became one of the first major bottlenecks. I experimented with training tokenizers directly on Turkish Wikipedia text, but repeatedly ran into issues related to vocabulary size, preprocessing stability, and memory constraints. I also discovered that many failures happened before actual model training even started. In practice, preparing the data pipeline turned out to be harder than launching the training itself. At various stages, I experimented with streaming-based approaches and alternative loading strategies to avoid memory problems. Some workflows partially worked, others failed unpredictably, and some became too slow to manage comfortably on my hardware. The deeper I went, the more I realized that low-resource experimentation was fundamentally a workflow engineering problem. The model architecture itself was only one part of the system. Why the Workflow Became Chained One of the biggest turning points came when I started splitting the datasets into smaller parts. Initially, I experimented with a small number of dataset chunks — around six parts — hoping sequential training could reduce runtime pressure and memory instability. The idea worked better than expected. Instead of treating training as one uninterrupted process, I could continue training progressively across multiple dataset parts. That realization slowly evolved into a chained training workflow. However, new problems appeared immediately. Some training parts took an extremely long time to finish. In several experiments, a single dataset part could run for dozens of hours. On my hardware, that created practical problems beyond machine learning itself. A training session running continuously for two or three days introduced risks such as: unexpected crashes overheating power interruptions unstable runtime behavior inability to use the machine normally during training At that point, the workflow design started changing again. I increased the number of dataset parts repeatedly: first 6 then 12 eventually around 120 smaller parts Counterintuitively, increasing the number of parts actually made experimentation more manageable. Smaller parts reduced runtime risk, improved recovery speed, and made debugging significantly easier. The project gradually shifted from: “Train a model.” to: “Build a system capable of surviving long-running local experiments.” That distinction ended up shaping the entire toolkit architecture. The Birth of the Training Loop An early chained training session showing tokenizer loading, dataset preparation, and the beginning of a local GPT-style training run. As the number […]