TOOLS / LATENT ATTENTION / TOOL #72

Enterprise Autonomous Agent Multi-Head Latent Attention (MLA) Low-Rank Projection Simulator

Simulate Multi-Head Latent Attention (MLA) low-rank KV-cache compression, decoupled RoPE vector isolation, and 90% VRAM reduction across DeepSeek-V3 and DeepSeek-R1 clusters.

MLA Low-Rank Compression Telemetry

Down-projects keys and values into a shared 512-dimensional latent vector and isolates decoupled 64-dimensional RoPE vectors, enabling up-projection matrix absorption directly into active query heads during inference decoding.

VRAM Compression: Over 90% Reduction
Active Elements/Tok: 576 vs 8,192 MHA
Stream Concurrency: Up to 512 Streams/Node