Scaling LLMs to 100K+ Tokens: How Ring Attention Shatters GPU Memory Barriers
Training LLMs on massive contexts like medical records requires overcoming crippling GPU memory limits. We dissect how Ring Attention, combined with FSDP and gradient checkpointing, enables 100k+ token sequences by distributing activations across GPUs—revealing critical PyTorch profiling insights and the 58% throughput trade-off for this breakthrough.