Developing In-House C and C++ Inference Engines: An Inside Look §0§
inference
| Source: Lobsters | Original article
Company develops custom C and C++ inference engines for improved performance. This approach reduces binary size and enhances depth estimation capabilities.
Developers are opting to write their own C and C++ inference engines, a trend that has significant implications for the efficiency and cost-effectiveness of AI model deployment. This approach allows for more control over the performance and size of the inference engine, as evidenced by successes such as reducing binary size from 9.1 GiB to 66 MiB and achieving depth estimation that surpasses PyTorch on CPU in half the memory.
Why it matters is that over the lifetime of a model, serving can be more expensive than training, making the need for custom inference engines crucial, especially with the mainstream adoption of large language models (LLMs). By building their own engines, companies can optimize for specific use cases, such as financial crime detection, and ensure regulated AI decisions are made with a tailored stack.
What to watch next is how this trend evolves, with more developers potentially turning to building custom inference engines in C++ and CUDA to meet the unique demands of their applications, and whether this shift leads to further innovations in AI model deployment and serving efficiency.
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