Inside the Runtime
From memory and compute pipelines to context management and assistant workflows, we design the full AI stack, and heren share our progress to drive the future of local intelligence together.
Inside the Runtime
Follow new releases, engineering breakthroughs, and examples of Local AI in action — all built to run closer to where your product lives.
On-Device Model Architecture: Where GPT-OSS Fits in the Edge AI Landscape
Edge devices have limited memory footprints, making Mixture of Experts (MoE) models with active parameter selection the optimal solution for deploying sophisticated AI reasoning locally. Active vs....
On-Device Model Architecture: Where GPT-OSS Fits in the Edge AI Landscape
Edge devices have limited memory footprints, making Mixture of Experts (MoE) models with active parameter selection the optimal solution for deploying sophisticated AI reasoning locally. Active vs....
Boosting Local Inference with Speculative Decoding
In our recent posts, we’ve explored how CPUs deliver impressive results for local LLM inference, even rivaling GPUs, especially when LLMs push on hardware's memory bandwidth limits. These bandwidth...
AI Inference at the Edge: A Deep Dive into CPU Workload Bottlenecks and Scaling Behavior
AI inference workloads on CPUs are mostly memory- bound rather than compute-bound, with performance bottlenecks arising from poor cache utilization, static thread scheduling, and suboptimal task...
Rethinking the CPU: Unlocking Hidden Performance for Client-Side AI Inference
When most people think of AI acceleration for client devices, they think GPUs. Some may nod to NPUs or specialized ASICs. But the CPU, the most ubiquitous compute unit in every device, rarely enters...
Client-Side Inference, Reimagined: Llama 4 Scout Goes Local
Client-Side Inference, Reimagined: Llama 4 Scout Goes Local Deploying large AI models across devices is hard. Llama 4 Scout, which we showcase here, typically wouldn’t fit on client devices. But with...
Unlocking the Full Potential of GPUs for AI Inference
GPUs are a cornerstone of modern AI workloads, driving both large-scale model training and real-time inference applications. However, achieving full utilization of these powerful accelerators remains...
OpenInfer Featured in VentureBeat: $8M to Revolutionize AI Inference at the Edge!
We are thrilled to announce that VentureBeat has covered our latest $8M funding round, highlighting our mission to redefine AI inference at the edge. OpenInfer is building the first-ever Inference OS,...
Introducing the First Preview Build of the OpenInfer Engine
We’re excited to announce the first preview build of our OpenInfer Engine—a powerful AI runtime designed to make on-device inference simple, seamless, and developer-friendly. This early release...
Introducing OpenInfer API: The Zero-Rewrite Inference Engine That Integrates Effortlessly Into Your Stack
At OpenInfer, our primary goal is to make integration effortless. We’ve designed our inference engine to be a drop-in replacement—switching your endpoints is as simple as updating a URL. And here's...
Introducing Performance Boosts in OpenInfer: 2-3x Faster Than Ollama/Llama.cpp
At OpenInfer, we strive to redefine the boundaries of Edge AI performance. Our latest update demonstrates a 2-3x increase in tokens per second (tok/s) compared to Ollama/Llama.cpp. This boost was...
Unlocking Efficiency: OpenInfer's Breakthrough in Memory Optimization
At OpenInfer, we're dedicated to pushing the boundaries of what's possible with large language models (LLMs). These models, while immensely powerful, often come with hefty hardware demands that can be...
Running large models and context within a small fixed memory footprint.
OpenInfer is on a mission to help AI Agents to run on any device. In this video one of our engineers Vitali shares a brief demo of how you can run large models and large context within a small fixed...
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