INT4 caching doubles long‑context attention throughput
Long‑context transformer serving can become bottlenecked at large token lengths (e.g., several thousand tokens), posing challenges for real‑time deployment. QuantMLA’s dual‑path INT4/INT2 cache shatters that wall, delivering over five times the whole‑job output throughput of a BF16 baseline when th…
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- 2026-10-21 05:00 · DEV Community — Machine Learning
INT4 caching doubles long‑context attention throughput