Smart cockpit large models are moving from basic voice responses to complex task interactions and multimodal understanding, but real-world deployment still suffers from slow response times, poor multi-turn coherence, and lag during multitasking. According to PCauto, the root cause lies in heavy computational pressure during the prefill phase, memory bandwidth sensitivity during the decode phase, and the need to share compute, bandwidth, and power with IVI rendering, multi-screen displays, and driver monitoring systems. This creates a 'four-fold constraint' of compute, bandwidth, power, and precision.
Since vehicle-side resources are inherently limited, simply stacking raw compute is not the only solution. Low-bit quantization reduces weight storage and data movement overhead, making it a key approach to improving on-device large-model deployment efficiency. However, at extremely low bit widths, quantization errors can degrade complex reasoning, long-text understanding, and multi-turn dialogue. Currently, INT4 is a major industry path, while FP4 uses floating-point representation that better matches weight distribution characteristics. With adapted quantization methods and hardware support, FP4 could achieve a new balance between performance and efficiency at ultra-low bit widths.
FP4 Doubles Compute Specs
Arm China's 'Zhouyi' X3 is the first cockpit chip in China to support FP4, doubling compute specifications compared to INT8/FP8, providing stronger low-bit computing power for large-model inference. Combined with the storage and data-movement advantages of low-bit weights, it helps optimize prefill-phase computational efficiency and alleviate decode-phase data access pressure.
The chip supports W4A16 and natively supports W4A8, offering flexibility for different low-bit quantization schemes. The combination of FP4 weights and FP8 activations enables the model to balance efficiency, overhead, performance, and user experience. The 'Zhouyi' NPU Compass AI software platform provides a full toolchain for model quantization, tuning, and deployment, along with ready-to-use demos to help customers adapt and optimize FP4 models for on-device deployment.
Soft-Hardware Co-Optimization
With FP4 as a key technology, the 'Zhouyi' X3 achieves software-hardware co-optimization from hardware compute to software toolchain, supporting efficient on-device large-model deployment. This helps smart cockpits strike a better balance among inference efficiency, model performance, and system resource overhead, moving large models from merely 'running' to efficiently deployed.
