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MSA demonstrates that a 109B-parameter model can process 1M-token contexts with 28.4x less attention compute and 14.2x faster prefill, making million-token agentic and code-reasoning workloads substantially more feasible at deployment scale.
Sparse attention research bottlenecks slow both human researchers and AI coding agents — Vortex's programmable serving layer removes that friction, enabling faster automated exploration of attention algorithms for long-context LLM deployments.