Slow CPU inference is a persistent drag on real-time apps and on-prem deployments: product teams at roughly 2 million businesses that collectively represent a $24.0B annual market spend on ML inference and optimization struggle with high latency, high cost, and privacy constraints when GPU-based options are impractical. These problems show up across industries — retail kiosks, manufacturing vision, enterprise on-prem analytics, and healthcare — where developers need sub-second inference but must avoid cloud GPU costs or data egress. You could build an end-to-end stack that co-designs tiny convolutional nets with an x86-aware compiler and highly tuned math kernels: automated NAS/quantization pipelines that produce models 2–4× smaller plus a runtime that exploits AVX512/AMX/oneAPI paths and auto-tuned microkernels for 2–10× speedups depending on workload. Deliverables would include a model zoo for common vision and audio tasks, an SDK for integration, and benchmarking tools that prove real-world latency and cost wins on commodity servers and edge PCs. This market is attractive now because three converging trends materially change the proposition: edge-first inference driven by privacy and latency, substantive CPU vector and matrix extensions that close the GPU gap, and steady progress in model efficiency techniques that preserve accuracy at much smaller sizes; the addressable $24B figure and the stated 2M potential customers indicate clear demand. The differentiator is tight model-to-kernel co-design and a developer experience that hides assembly-level tuning, which is defensible IP, but expect real engineering challenges from x86 instruction-set fragmentation, hardware vendor partnerships, and competition from large ML infra players — success will require focused vertical use cases and measurable TCO reductions to win adoption.