We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts.

InferX's AI-native architecture, with its "snapshot" technology, enables:

* *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization.

This isn't just another API; it's a new execution layer designed from the ground up for the unique demands of LLM inference. We're seeing strong interest from infrastructure teams and AI platform builders.

Would love your thoughts and feedback! What are the biggest challenges you're facing with LLM deployment?

Demo: https://inferx.net/