Building the Compute Cluster
Training a frontier LLM requires thousands of GPUs working in perfect coordination. This infrastructure is one of the most expensive and complex parts of the entire project.
An Orchestra of GPUs
Training an LLM is like conducting a massive orchestra where thousands of musicians (GPUs) must play in perfect synchronization. If one goes out of tune or pauses, the entire performance suffers.
10K+
H100 GPUs
400 Gbps
Interconnect
100+ PB
Storage
99.9%
Uptime Required
Parallelism Strategies
📊
Data Parallelism
Same model on all GPUs, different data batches
🔲
Tensor Parallelism
Split individual layers across GPUs
🔗
Pipeline Parallelism
Different layers on different GPUs
📏
Sequence Parallelism
Split long sequences across GPUs
Key Frameworks
DeepSpeed(Microsoft)
ZeRO optimizer, memory efficiencyFSDP(PyTorch)
Fully Sharded Data ParallelMegatron-LM(NVIDIA)
3D parallelism, efficient attentionJAX/Pax(Google)
TPU optimization, XLA compilation✅
Key Takeaways
- Large LLMs require 1000s of GPUs with fast interconnects
- 3D parallelism combines data, tensor, and pipeline parallelism
- DeepSpeed ZeRO dramatically reduces memory requirements
- Checkpointing is critical for recovering from failures