Article by Ayman Alheraki on January 11 2026 10:36 AM
Large Language Models (LLMs), such as ChatGPT and other cutting-edge AI systems, require a sophisticated ecosystem of tools, languages, and storage systems. These components work together to support model training, deployment, and operation at scale. Below is a detailed breakdown of the most essential tools and technologies powering modern LLMs.
Python Python is the primary language used for developing and training LLMs. Its simplicity, readability, and vast ecosystem of AI libraries (like PyTorch, TensorFlow, and Hugging Face Transformers) make it ideal for experimentation and model design.
C++ Many performance-critical operations, such as matrix multiplications and GPU memory handling, are implemented in C++. Libraries such as PyTorch and TensorFlow use C++ under the hood to accelerate runtime and optimize low-level computations.
CUDA CUDA is essential for utilizing NVIDIA GPUs during training and inference. It allows developers to write parallel code that maximizes GPU performance.
Triton Developed by OpenAI, Triton is a newer framework designed to write highly optimized GPU kernels more easily than with raw CUDA. It offers Python-like syntax and can achieve performance comparable to handcrafted CUDA code, especially in LLM workloads.
Rust and Go These languages are sometimes used in infrastructure components, such as data pipelines, logging systems, or auxiliary tools. Rust offers memory safety and performance, while Go is well-suited for scalable backend services.
Unlike traditional applications, LLMs do not rely on classic relational databases like MySQL or Oracle. Instead, they utilize a combination of high-performance data systems and specialized file formats optimized for training at scale.
In-Memory Storage Redis and Memcached are often used to cache results, manage temporary data, and enable fast access in real-time environments.
Distributed File Systems and Object Storage Amazon S3, NFS, and Ceph are widely used to store the massive datasets required for LLM training. These systems are built for high throughput and parallel access across multiple compute nodes.
Experiment Tracking and Data Versioning Tools like Weights & Biases, MLflow, and DVC (Data Version Control) are used to track experiments, monitor metrics, manage datasets, and share model artifacts. These tools are essential for reproducibility and team collaboration.
Model Storage Formats Trained LLMs are saved using binary formats such as:
.pt for PyTorch
.bin as a generic binary format
.safetensors, a newer format focused on performance and safety
These formats support fast loading and deployment across hardware platforms.
Programming Stack: Python is the primary language, with performance-critical components implemented in C++, CUDA, and Triton.
Data Layer: LLMs do not use traditional databases. Instead, they rely on distributed file systems, in-memory caches, and experiment tracking tools.
Storage: Models are stored in efficient binary formats suitable for high-speed inference.
There is no single database powering LLMs. Instead, they operate through a distributed and optimized architecture tailored for large-scale machine learning.