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Multi AI-communications
Welcome to telet.ai labo
telet Multi Communications. We are committed to research and development of next-generation efficient AI models.
Contact Ustelet Multi Communications. We are committed to research and development of next-generation efficient AI models.
Contact UsModern AI development faces serious structural challenges: the monopolization by giant models and their supporting computational resources. Many excellent researchers and developers are constrained by the latency and cost of black-boxed APIs and cannot access the internal behavior of models. This situation represents nothing but a loss of innovation opportunities.
We raise a clear flag of rebellion against this centralized paradigm. Based on our unwavering belief that the true value of AI lies not in the enormity of parameters but in its 'resource efficiency', we lead the next generation AI revolution. Our mission is to liberate AI execution environments from large data centers and make them ubiquitous across edge, mobile, and even personal devices. This is not merely a story of cost reduction, but a fundamental challenge to democratize AI development and create new application ecosystems.
The core of our research and development lies in advanced technologies that reduce computational costs (FLOPs) and memory footprints by orders of magnitude while maintaining or improving model accuracy. Specifically, this includes not only static structured pruning and non-uniform quantization (INT4/INT2), but also dynamic sparse optimization that dynamically optimizes computational paths during inference.
These technological breakthroughs open up application areas that were previously products of fantasy: interactive systems where real-time performance is absolutely essential, field agents that autonomously make situational judgments in offline environments, and above all, personal AI that operates with complete privacy in users' hands.
Standard vector search is high-cost and high-latency. We build 'hierarchical semantic clustering' that dynamically limits vector space based on queries. By combining coarse cluster search with detailed search within subspaces, we reduce computational complexity by orders of magnitude, achieving high-speed, high-precision search even on small-scale resources.
Autoregressive token generation in LLMs involves redundant computational paths. We research 'inference path distillation' where expert decision logic is pre-trained into models as computational graphs. This approach combines INT8/INT4 quantization to avoid zero-based reasoning during inference and maximize response performance.
Monolithic giant models suffer from reliability issues like hallucination. We design clusters of lightweight LLM agents specialized for tasks including search, analysis, and verification. They share and coordinate knowledge on distributed blackboard systems, generating robust and reliable outputs through consensus formation.
Lightweight High-Speed Inference Engine
Collaborative Multi-LLM System
Modern AI development has been consolidated into a few organizations with massive GPU clusters and enormous capital. Many developers are limited to using models via APIs. This represents a loss of 'AI sovereignty' due to technical and economic barriers.
Our research provides the technical foundation to break down this centralized structure. Our goal is to create an ecosystem where anyone can build and operate purpose-specific 'sovereign AI' based on their own data, just like building a website.
Ultra-efficient inference engines enable advanced AI execution on consumer hardware. Intelligent indexes provide frameworks for injecting domain knowledge into AI without retraining costs. This is not just model development, but an attempt to architect new standards for AI development: a shift towards distributed, democratized development paradigms.
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