Semantic Memory Management for Faster Inference

Today, AI performance is no longer limited by how fast GPUs can compute — it’s

limited by how inefficiently memory is used during inference. We’re building technology that unlocks the

hidden efficiency layer, turning existing GPU clusters into significantly higher-throughput

inference engines — without retraining models or purchasing new hardware. AI model sizes and

context lengths are growing far faster than data-centre efficiency. Every additional

token increases memory pressure, bandwidth usage, and cost. As a result,

inference is becoming the dominant operational expense — not training. The industry is pouring capital

into GPUs, but the returns are diminishing because memory inefficiency is silently taxing