Part 0 of 6 in the Distributed Training series
0.1 Intended Audience
This series assumes familiarity with PyTorch and basic neural network training. You should understand what loss.backward() does, how optimizers update parameters, and why GPUs are useful for matrix operations. Experience with transformers helps but isn't required; the distributed training concepts apply to any model architecture.
You don't need prior experience with distributed systems or multi-GPU training. That's the point: these posts build the primitives from scratch, explaining not just how to use abstractions like DDP and FSDP, but why they exist and what constraints they're solving.
If you've ever wondered why gradient synchronization matters, how ZeRO reduces memory usage, or what tensor parallelism actually means at the implementation level, this is for you.
0.2 Motivation
This project grew out of frustration. After building Ryan-GPT (a 12.5M parameter transformer trained on a single RTX 3060), I hit the wall that everyone hits: the model fit, but barely. Gradients, optimizer states, and activations consumed most of the 12GB VRAM. Scaling further meant distributing work across devices.
The documentation for distributed training told me which functions to call. It didn't tell me what would break at scale, why certain approaches exist, or how to reason about the tradeoffs between memory, compute, and communication. Every tutorial used 8 A100s as the baseline example. I had one consumer GPU and wanted to understand the systems that would eventually run on clusters I couldn't afford.
So I built them. Not wrappers around existing frameworks. The actual primitives, from gradient synchronization to optimizer sharding to tensor parallelism. All validated on a single GPU using multi-process simulation. The goal was to understand them well enough to know when they'd fail and why.
0.3 Series Overview
Part 1: Why Single-GPU Training Doesn't Scale: Understanding the constraints before buildling distributed systems.
Part 2: Data Parallelism as the First Scaling Primitive: Explicit gradient synchronization and the cost of communication.
Part 3: Bucketing and Sharding as the Second and Third Scaling Primitives: Reducing communication overhead and memory usage.
Part 4: Tensor Parallelism as the Fourth Scaling Primitive: Splitting individual layers across GPUs.
Part 5: Training Validation on a Single GPU Distributed code without distributed hardware?
Part 6: Tests Results and Future Directions: Crunching the numbers and what comes next.