Search Results: LongContextLLMs

New research reveals fundamental limitations in how large language models process long contexts, demonstrating that popular inference-time scaling techniques fail beyond certain thresholds. Researchers propose test-time training with targeted gradient updates as a more effective approach, showing double-digit performance gains.

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.