Deploy powerful language models on edge devices with 95% memory reductionand 87% performance retention. Perfect for IoT, mobile, automotive, and industrial applications.
Bring the power of large language models to resource-constrained environments with unprecedented efficiency.
Compress models from 50GB to just 50-500MB while maintaining performance
Ultra-fast processing with real-time responses for edge applications
Extremely efficient compared to 500W+ cloud inference requirements
Local processing eliminates data transmission risks and privacy concerns
Function without internet connectivity for critical applications
Eliminate ongoing cloud API costs with one-time deployment
Detailed performance metrics and capabilities of our Tiny LLM implementations.
Real-world applications of Tiny LLMs across industries with proven results.
Smart sensors and equipment with AI-powered predictive maintenance and anomaly detection
Portable diagnostic equipment with real-time AI analysis and patient monitoring
In-vehicle AI processing for autonomous driving, safety systems, and infotainment
Intelligent mobile apps with local AI processing for enhanced user experience
Production line optimization with AI-powered quality control and process automation
In-store AI for inventory management, customer analytics, and personalized experiences
Real implementations with measurable business impact and technical achievements.
Our proven methodology ensures successful Tiny LLM deployment with minimal risk and maximum performance.
Tiny LLMs are compressed versions of large language models optimized for edge devices. They use techniques like quantization, pruning, and distillation to reduce model size by 95% while retaining 87% of the original performance, enabling deployment on resource-constrained devices.
Tiny LLMs can be deployed on various platforms including Raspberry Pi, ESP32, NVIDIA Jetson, mobile devices (iOS/Android), industrial controllers, automotive ECUs, and custom edge computing hardware. We optimize for your specific hardware requirements.
We use advanced techniques including knowledge distillation, structured pruning, dynamic quantization, and hardware-aware optimization. Our models typically retain 85-95% of original accuracy through careful optimization and validation processes.
Tiny LLM implementation typically takes 6-12 weeks depending on complexity: 1-2 weeks for assessment, 3-4 weeks for model development, 2-3 weeks for integration, and 1-2 weeks for deployment. We provide detailed project timelines during planning.
Yes, Tiny LLMs are designed for offline operation. Once deployed, they function independently without internet connectivity, making them ideal for remote locations, secure environments, or applications requiring 100% uptime.