Access the OpenInfer Runtime Infrastructure
Architecture Whitepaper to learn how we’re
redefining inference at the edge
Turn CPU, GPU, and distributed machines into a unified inference fabric with shared memory and scaling. Keep data private and latency low while avoiding monolithic upgrades.
Unify heterogeneous edge devices so they cooperate intelligently, share context, and scale workloads across constrained networks — resilient even when connectivity drops.
Run fast, private, and context-rich models locally while seamlessly bursting to cloud when available. Reduce cost and dependency while improving responsiveness.
Follow new releases, engineering breakthroughs, and examples of Local Al in action - all built to run closer to where your product lives.
LLM inference feels slow because decode is expensive at scale. Prefill runs once, but decode runs per token—overhead multiplies across the entire output. We address this by optimizing the decode loop,...
Today, we’re excited to share a big step forward for OpenInfer: we’ve officially joined the Intel® Partner Alliance and Microsoft’s Pegasus Program. These are two of the most influential innovation...
Porting desktop code to mobile often breaks because mobile file systems are sandboxed and heavily restricted compared to desktop environments. Operations like using relative paths, hardcoded...
