CoreWeave Leads on Artificial Analysis for Kimi K2.7 Code Inference | CoreWeave Blog

What this post added

This post details the engineering work involved in optimizing the Kimi K2.7 Code model for inference on CoreWeave's Blackwell GPU infrastructure. Key contributions include: implementing NVFP4 quantization by converting INT4 weights using NVIDIA Model-Optimizer with a custom 3-pass calibration (short context, long context math, long context coding), training a DFlash speculative decoding model using a generated dataset and the D-PACE loss function for improved acceptance length, and contributing support for D-PACE to the TorchSpec project and for AL evals to AIPerf. The entire process was managed on CoreWeave Kubernetes Service with RoCE fabric for fast training, and the optimized model is deployed on CoreWeave Inference via vLMM.