How to train a model across thousands of GPUs
Training a frontier model like GPT-4 or Claude requires thousands of GPUs working in concert for months. No single machine can hold the model in memory, process the data fast enough, or complete training in a reasonable time. Distributed training solves this.
The challenge is both computational and logistical: split the work across machines, keep them synchronized, handle failures gracefully, and do it all efficiently enough that you're not wasting millions of dollars on idle GPUs.
The simplest approach: copy the full model to every GPU, split the training data into batches, each GPU processes its batch, then gradients are averaged across all GPUs. Works when the model fits in one GPU.
When a model is too large for one GPU, split individual layers across GPUs. Each GPU holds a slice of each layer. Requires high-bandwidth interconnects (NVLink) between GPUs.
Split the model by layers: GPU 1 gets layers 1-24, GPU 2 gets layers 25-48. Data flows through the pipeline. Micro-batching keeps all GPUs busy instead of waiting.
Use FP16 or BF16 for most computation (2x faster, half memory) but keep a FP32 master copy of weights for numerical stability. Nearly free speedup.
Instead of storing all activations for backpropagation (huge memory cost), recompute them during the backward pass. Trades compute for memory - enables training larger models on fewer GPUs.
With thousands of GPUs running for months, hardware failures are inevitable. Checkpointing saves model state periodically. Elastic training handles node failures without restarting.
ZeRO optimizer, 3D parallelism, offloading - trains trillion-parameter models
Fully Sharded Data Parallelism - native PyTorch distributed training
Distributed training orchestration - fault-tolerant, multi-framework
Large-scale transformer training with tensor and pipeline parallelism
Compiler-based parallelism - automatic sharding across TPU pods
High-bandwidth GPU interconnects - 900 GB/s between GPUs in a node
NVLink, NVSwitch, DGX SuperPOD - the hardware backbone of distributed training
Ray framework used by OpenAI, Anthropic, and Uber for distributed workloads
DeepSpeed powers training at scale. Azure's ND H100 clusters for large runs.
TPU v5p pods - 8,960 chips in a single training cluster. Trains Gemini.
Key Takeaway
Distributed training is why only a handful of companies can build frontier models - it requires not just GPUs but deep systems engineering expertise. The frameworks (DeepSpeed, FSDP, Megatron) are the unsung heroes making it possible.