Nexify AI Core Journey
Orchestrating next-generation custom AI model architectures, fine-tuning local Small Language Models (SLMs), and deploying hybrid agentic routing pipelines using Nvidia Cloud GPUs.
timeline Growth Timeline
verified_user Skills Mastered
cached Currently Learning
- Deploying on-device models optimized for hardware RAM
- Designing hybrid routing between nexify i2 & i4 models
- Reducing token context footprint through prompt subclassing
checklist Roadmap Checklist
Nexify 2.5 Pro & v3 Flash
First ML models (took 2 months of training)
Nexify 3.5 Pro Training
Heavy model compilation on Nvidia Cloud
Hybrid i2 + i4 Routing Pipeline
Developing prompt looping structures
edit_note Garden Learning Notes
* The resulting 5 GB local model is optimized to run directly on system RAM, minimizing network dependency.
* LangChain acts as the primary pipeline manager, facilitating state looping, prompt inheritance, and structured API routing.
Building Nexify AI has been a massive 6-month journey into the mechanics of neural weights and custom agent routing. Crafting a 5 GB model that runs locally on RAM while scaling larger models like 3.5 Pro and Hybrid i2+i4 on Nvidia hosting highlights the future of distributed, hybrid AI execution.
Favorite Resource Guides
Hand-selected reference documentations