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tech 3 August 2026

Why We Write Our Own C and C++ Inference Engines

Explore why LocalAI opts to develop its own inference engines in C and C++. Reduced memory footprint, enhanced performance, and improved compatibility: dive into the benefits and challenges of this approach.

Article inspired by the original source
Why we write our own C and C++ inference engines ↗ localai.io

Introduction

In a world where AI and machine learning dominate the tech landscape, companies are constantly seeking optimization. LocalAI, an innovative player in the AI field, has chosen a distinct approach: developing its own inference engines in C and C++. But why take this path when numerous open-source solutions are available?

Reducing Memory Footprint

Size matters. In the realm of inference, managing memory is a major challenge. By choosing to develop our own engines, we have significantly reduced the memory footprint. For instance, our C++ port of the vLLM V1 architecture weighs only 66 MiB, compared to 9.1 GiB for its Python equivalent. This drastic reduction not only enhances efficiency but also facilitates deployment on diverse infrastructures.

Enhanced Performance

Performance is critical in AI. Our C and C++ engines demonstrate top-notch performance, often rivaling, if not surpassing, established engines like vLLM. With a slightly higher token-per-second throughput, our approach optimizes resources without sacrificing speed. For example, using the same hardware configuration, our engine delivers token-for-token identical results to vLLM, all while using less memory.

Improved Compatibility

In the world of inference, hardware compatibility is often a hurdle. Heavy dependencies and specific hardware requirements can limit accessibility. By developing our engines in C and C++, we've managed to avoid these limitations. This enables us to deploy our solutions on a variety of machines, from high-end servers to more modest embedded devices.

Costs and Challenges

Of course, this approach is not without challenges. Developing custom inference engines requires resources in terms of time and talent. However, the benefits in terms of performance, compatibility, and reduced memory footprint more than justify these investments.

Conclusion

By creating our own inference engines in C and C++, we have not only optimized our operations but also opened new possibilities for the future of AI. If you're ready to explore how these innovations can transform your business, let's discuss your project in 15 minutes.

Let's discuss your project in 15 minutes.

LocalAI's C and C++ inference engines are not just a technological feat; they represent a commitment to constant optimization and innovation. Join us to discover how we can turn your challenges into opportunities.

C++ inference engines AI performance memory optimization hardware compatibility LocalAI innovation
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