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
Ready to experience AI?

Power your business, workflows, and engineers with Liquid AI

Manage your preferences

We use cookies to enhance your browsing experience and analyze our traffic. By clicking "Accept All", you consent to our use of cookies.

Learn more
  • Essential cookies required