NVIDIA Breakthrough Enables AI Models to Handle Million-Token Contexts 35x Faster Than Current Methods

NVIDIA researchers unveil TTT-E2E, a revolutionary AI method that compresses million-token contexts into model weights, delivering 35x faster processing speeds for massive datasets while maintaining constant inference times regardless of context length.

• NVIDIA researchers develop TTT-E2E, a method that enables LLMs to compress long context into model weights during test-time through next-token prediction, achieving better performance than traditional transformers and RNNs
• TTT-E2E maintains constant inference latency regardless of context length, delivering 2.7x speedup over full attention for 128K context and 35x speedup for 2M context on H100 GPUs
• The method uses meta-learning during training to prepare model initialization, but current implementation runs 3.4x slower than standard pre-training due to FlashAttention limitations with gradient computations