BMAC provides CPU‑native, DRAM‑free inference hardware designed specifically for AI inference workloads. Its architecture eliminates reliance on high‑bandwidth memory, reducing memory movement and improving deterministic execution, deployment density, and efficiency across a range of model sizes. The solution is offered as rack‑mount accelerators for cloud datacenters and as dedicated modules for OEM/ODM integration, enabling seamless deployment in existing server environments without extensive redesign.
Funding
Funding not disclosed
Founders
Product
Problem
AI inference workloads typically rely on accelerators that require high‑bandwidth memory (HBM) and extensive data movement between CPU and external memory, leading to high system cost, complexity, and limited deployment density. This creates barriers for cloud providers, OEMs, and edge device manufacturers who need efficient, scalable inference without redesigning their infrastructure.
Solution
BMAC offers inference‑focused hardware that executes AI models directly on CPUs without the need for HBM or large memory transfers. Its rack‑mountable accelerators and dedicated integration modules plug into existing datacenter, server, and edge platforms, providing deterministic execution and high deployment density. By eliminating dependence on external memory bandwidth, BMAC reduces system complexity and power consumption while supporting a wide range of model sizes and types. The architecture is designed for flexible integration across cloud, OEM/ODM, mobile, and secure computing markets, enabling customers to add AI inference capability without extensive hardware redesign.
Target Audience
Primary customers are cloud infrastructure operators, OEM/ODM server manufacturers, and edge device developers seeking scalable, low‑complexity AI inference solutions.
Features
- CPU‑native inference engine that runs AI workloads without HBM, removing costly memory movement
- Deterministic execution guarantees consistent latency for real‑time applications
- High deployment density through rack‑mountable accelerator form factor
- Architectural efficiency that lowers power and overall system cost
- Flexible integration pathways for datacenter racks, server modules, and edge silicon
- Support for diverse model sizes and types, from large cloud models to mobile edge networks