A new technical series on the "Cocoa with Love" blog details the process of training Large Language Models (LLMs) using the Swift programming language. The initial installment focuses on accelerating matrix multiplication, a fundamental operation in neural network development, to improve training efficiency.

The author works to scale performance from Gflop/s (billions of floating-point operations per second) to Tflop/s (trillions of floating-point operations per second). This optimization effort addresses computational bottlenecks encountered when implementing tensor operations without relying on established C or C++ backends.

This work serves as a technical foundation for developing LLM training pipelines natively within the Swift ecosystem, moving beyond standard application-layer implementations.

A new technical series on the "Cocoa with Love" blog details the process of training Large Language Models (LLMs) using the Swift programming language. The initial installment focuses on accelerating matrix multiplication, a fundamental operation in neural network development, to improve training efficiency.

The author works to scale performance from Gflop/s (billions of floating-point operations per second) to Tflop/s (trillions of floating-point operations per second). This optimization effort addresses computational bottlenecks encountered when implementing tensor operations without relying on established C or C++ backends.

This work serves as a technical foundation for developing LLM training pipelines natively within the Swift ecosystem, moving beyond standard application-layer implementations.