We are pioneering efficient attention architectures that scale to 1 million tokens and beyond. Our research enables models to maintain complete conversation history, task context, and document understanding throughout extended interactions.
Rather than approximating attention through sparse patterns or hierarchical methods, we have developed techniques for efficient attention that scales to million-token contexts. This preserves the model's ability to attend to any part of the context without artificial limitations.
Our methods combine algorithmic innovations with hardware-aware optimizations to achieve practical inference speeds even with extremely long contexts.
With million-token contexts, our models can maintain coherent task memory across entire work sessions. Users can have continuous conversations spanning hours or days without losing context or having to remind the model of previous interactions.
This capability enables sophisticated applications like long-term personal assistants, extended collaborative coding sessions, and comprehensive document analysis.
Extended contexts unlock powerful in-context learning capabilities. Our models can learn new tasks from extensive examples, adapt to user preferences, and develop specialized knowledge within a single session.
This approach eliminates the need for fine-tuning for many applications, enabling rapid deployment and personalization.
We have developed custom kernels and training frameworks optimized for long-context scenarios. These optimizations span memory management, attention computation, and gradient calculation, enabling practical training and inference.
Our implementations leverage both NVIDIA CUDA and Google TPU capabilities for maximum efficiency across platforms.