## The challenge

A global automaker wanted to bring **real-time voice and vision AI** to vehicles—but off-the-shelf models were too slow for mid-tier CPUs. Despite months of effort with llama.cpp, **slow inference speeds** and **hardware limitations** blocked deployment.

Key Obstacles

- **Performance bottlenecks:** Small VLMs ran too slowly on existing hardware
- **Integration hurdles:** Unacceptable time-to-first-token (TTFT) for in-car UX
- **Resource constraints:** Couldn’t support efficient AI inference without costly upgrades

## Our solution

Liquid AI delivered a **hardware-optimized VLM** that ran **10x faster** on the automaker’s existing CPUs. Using our **Edge SDK**, we reduced model size by **50% without sacrificing accuracy**—and deployed a production-ready solution in **just one week**.

## The results

The automaker achieved **real-time AI interactions** directly in vehicles—no hardware upgrades needed.

- 10x faster time-to-first-token
- 50% smaller model size (no performance loss)
- Deployment slashed from months to 1 week
- Enabled real-time voice/vision AI on existing hardware