How to build a day-0 API for Kimi K3

Baseten announced day-0 support for Kimi K3 on their Model APIs, highlighting the collaborative efforts with Moonshot AI, Inferact, and RadixArk in developing this new open frontier model. With 2.8 trillion parameters, Kimi K3 is significantly larger than previous models, presenting various challenges in building a performant inference API. Utilizing new architectural techniques like Kimi Delta Attention and Attention Residuals, the model scales beyond the trillion-parameter threshold, leveraging extremely sparse experts and a novel vision encoder. The development process involved hardware provisioning, loading substantial model weights, and establishing baseline performance with leading open-source inference engines such as vLLM and SGLang. Rigorous benchmarking, facilitated by tools like the Kimi Vendor Verifier, ensured high-fidelity model serving, while extensive configuration and performance optimization strategies, including Tensor and Expert Parallelism, were employed to enhance latency and throughput. The large-scale deployment across multiple regions and cloud providers incorporates KV-aware routing to optimize system-wide throughput, crucial for handling the anticipated high demand for Kimi K3's capabilities in various applications ranging from coding to video editing.

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