Slow CPU inference hurting apps — tiny CNNs optimized for modern x86

Slow CPU inference hurting apps — tiny CNNs optimized for modern x86 targets a $24.0B = 2M businesses x $12K ACV (global ML inference & optimization spend across cloud/on-premise) total addressable market with medium saturation and a year-over-year growth rate of 22% CAGR for ML deployment & inference tooling.

Key trends driving demand: Edge-first inference -- privacy, latency, and bandwidth limits push workloads off cloud GPUs to CPUs at the edge or on-prem.; CPU performance parity improvements -- modern x86 vector instructions and matrix extensions enable significant NN acceleration without GPUs.; Model efficiency research -- distillation, quantization, and architecture search yield compact models with near-SOTA accuracy.; Open runtimes & compilers -- maturation of ONNX Runtime, TVM, and OpenVINO lowers engineering cost to ship optimized CPU inference..