Silicon Valley startup, iFrame™ AI, has quietly secured a nearly $20 million deal with a leading cloud provider to launch the world’s first Large Attention Model with an infinite context window – a breakthrough poised to disrupt the professional services industry and undercut OpenAI-like companies’ revenue from costly, redundant data retrieval services.
Much like DeepSeek shook the AI ecosystem last year, iFrame’s Asperanto and Sefirot-10 models eliminate the need for retrieval pipelines and fine-tuning altogether, fulfilling a recent prediction made by former Google CEO Eric Schmidt that infinite context models are on the horizon, aiming to reshape our understanding of agentic AI applications.
For nearly a decade, the artificial intelligence industry has been trapped inside the transformer’s attention matrix – the engine that powers every major AI from OpenAI, Google, and Anthropic, forcing even the most advanced models into a state of digital amnesia.
After three years of stealth mode, iFrame™ is launching the world’s first Large Attention Model (LAM), an architecture that doesn’t just stretch the context window — it makes the very concept obsolete. By removing the attention matrix entirely, iFrame™ has created a model that can natively reason terabytes of data in a single pass: no RAG, no fine-tuning, no parlor tricks. Instead of training a multibillion-dollar LLM to distill and fine-tune it for usable inference, with iFrame™, you simply upload terabytes of data to an attention block, upgrading AI knowledge in seconds.
“For better or worse, I helped AI to escape the Matrix — literally,” says Vlad Panin, iFrame’s founder and creator of the Monoidal Framework, in the recent interview. His breakthrough didn’t come from iterating on existing AI research, but from deep work on the mathematics of universe topology, inspired by the work of the famously reclusive Grigori Perelman, who solved the Poincaré Conjecture in 2002.
“Everyone is trying to optimize the matrix from within its accepted narratives,” Panin explains. “I was reckless enough to search for a key to a door whose existence is explicitly ruled out by the doctrine of matrix calculation parallelism.”
This is a fundamental challenge to the entire AI hardware and software ecosystem. GPU powerhouses like AWS, Azure, and Google can potentially quadruple their datacenter utilization capacity overnight. iFrame’s architecture is designed from the ground up to operate on decentralized networks, utilizing every bit of available memory across available hardware. It sidesteps the GPU VRAM bottleneck that has made NVIDIA the king of AI and opens a path to a world where massive AI models run on a global network of distributed devices.
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