For December 2025, the vLLM-Ascend repo (vllm-project/vllm-ascend) delivered a key feature: W4A16 quantization for the Kimi-K2-Thinking model, improving weight packing/unpacking efficiency and supporting new quantization parameters to boost model efficiency. The work included implementing the complete W4A16 quantization method (weight packing/unpacking, per-group quantization parameter generation, post-processing logic, and MoE method application), adding new configuration parameters use_int4_w4a16, w1_offset, and w2_offset, and updating the with_quant logic to support W4A16 matrix multiplication. It also added a packed_modules_model_mapping for the Kimi-K2-Thinking model and processing logic for the weight_packed field. The change aligns with vLLM v0.12.0 baseline and references the commit ce5872705e80d3e2fb107808aa296831d93fe6fa and PR #4516. No major bug fixes were reported this month for this repo; the primary focus was feature delivery aimed at improving model efficiency and enabling deployment on Ascend hardware. Impact includes improved throughput and reduced memory footprint, enabling larger models to run efficiently on constrained hardware. Demonstrates skills in quantization techniques (W4A16), MoE integration, per-group quantization, parameterization, and cross-team collaboration.