← All updates
llm
Why Linear Attention for 2M Context
Quadratic attention does not scale to million-token workloads. Continuum's linear layers keep latency predictable for production pipelines.
Innomium LLM Team · February 20, 2026
Teams hit a wall when document QA, log analysis, and multi-file code review outgrow 128k context windows.
The bottleneck
Standard transformer attention grows quadratically with sequence length. That breaks cost models long before it breaks GPUs.
Continuum's answer
Fully linear decoder layers with anchor full-attention blocks deliver:
- Predictable memory on long sequences
- Custom Triton kernels in
continuum-flash-linear-attention - NOPE for stable length extrapolation
The Continuum Kernel Optimization task on Innomium Arena invites systems engineers to push inference speed further.