msmodelslim

To quantize large language models for Ascend NPU deployment, you use the msModelSlim toolchain to compress models via W4A8 or W8A8 schemes, prepare calibration data, and export to MindIE or vLLM-Ascend.

Supported quantization formats for multimodal models on Ascend hardware include W4A8, W8A8, W8A8C8, W8A8S, and W16A16S, allowing you to balance memory usage, latency, and accuracy.

Yes, you need a compatible Python and CANN environment along with the PyTorch dependency to run model calibration, precision auto-tuning, and sensitive layer analysis on Ascend hardware.

You can integrate custom model architectures into the Ascend quantization workflow by using provided adapters, configuration templates, and config management to handle model-specific compression.

Sparse quantization and KV-Cache quantization for MoE models work by applying optional configuration templates during calibration to reduce memory and latency while preserving accuracy on Ascend NPUs.

The best way to deploy quantized models using vLLM-Ascend or MindIE is to run calibration and precision auto-tuning, then use built-in deployment scripts to export the optimized model directly.