How Karpathy Built a 1,000‑Line C LLM Trainer Without Any Deep‑Learning Framework

Overview

LLM.C is a minimalistic implementation of a GPT‑2‑style transformer language model written entirely in C (with optional CUDA kernels). The codebase is about 1,000 lines and does not depend on any external deep‑learning framework.

Implementation details

The project targets three main goals:

Train large language models directly in C/CUDA with throughput comparable to PyTorch.

Accelerate the CPU version using SIMD extensions such as AVX2 (x86) and NEON (ARM).

Provide a foundation that can be extended to newer architectures like Llama 2 and Gemma.

Key technical choices:

All required memory (weights, activations, gradients) is allocated in a single contiguous one‑dimensional array at program start, keeping runtime memory usage constant across batches.

Each transformer layer (attention, feed‑forward, layer‑norm, etc.) has hand‑written forward and backward functions that are explicitly chained together. For example, the forward and backward passes of layer‑normalization are implemented in plain C without any library calls.

Weights and intermediate tensors are accessed via pointer arithmetic that maps logical tensor indices to offsets inside the unified memory buffer.

Performance extensions

Future work includes:

Porting each layer to CUDA kernels to reduce GPU latency and approach or exceed PyTorch performance while avoiding heavy dependencies.

Reducing numerical precision from fp32 to fp16 or lower to improve memory bandwidth and speed.

Adding optional layers such as RoFE to support more advanced transformer variants.

Resources

The full source code, build instructions, and a migration guide from PyTorch to C are available on GitHub:

https://github.com/karpathy/llm.c