BitTP: The Lightweight Trajectory Prediction Model with BitLLM for Edge-Devices

Researchers introduce BitTP, a quantization technique that compresses LLM-based trajectory prediction models to 1.58-bit weights while maintaining full-precision activations, enabling deployment on resource-constrained edge devices. The approach not only reduces memory and latency but actually improves prediction accuracy by 14-21% compared to full-precision baselines, demonstrating that strategic quantization can serve as an effective regularizer.

BitTP addresses a critical challenge in deploying sophisticated AI systems on edge devices—the computational overhead of large language models. While LLMs excel at trajectory prediction for autonomous systems through contextual reasoning, their memory and compute demands make real-time inference on embedded hardware impractical. This research demonstrates that aggressive weight quantization to 1.58-bit precision, combined with full-precision activations, creates an optimal efficiency-accuracy tradeoff.

The key innovation lies in identifying which components can be quantized without degradation. Quantizing activations severely destabilizes spatio-temporal reasoning, revealing that not all model parameters contribute equally to prediction quality. This selectivity distinguishes BitTP from naive quantization approaches that uniformly reduce precision across all dimensions.

For autonomous robotics and edge AI applications, this development has substantial implications. Deploying LLM-based reasoning directly on vehicle computers eliminates latency from cloud communication and reduces bandwidth requirements. The paradoxical improvement in prediction accuracy suggests that quantization acts as implicit regularization, reducing overfitting to training data.

The broader industry trend shows increasing focus on making large models practical for decentralized deployment. As autonomous systems proliferate—from robotics to autonomous vehicles—the ability to run sophisticated reasoning locally becomes economically and operationally essential. BitTP exemplifies how hardware constraints drive algorithmic innovation rather than limiting capability. Future developments may explore whether similar quantization patterns apply to other multimodal reasoning tasks, potentially accelerating edge deployment of diverse AI applications across industrial and consumer domains.

• →BitTP achieves 1.58-bit weight quantization while maintaining full-precision activations for trajectory prediction on edge devices
• →The model reduces memory usage and inference latency while improving prediction accuracy by 14-21% over baseline LLM approaches
• →Quantization acts as an effective regularizer, improving generalization rather than degrading it when applied selectively
• →Spatio-temporal reasoning requires preserved activation precision, demonstrating that not all model components tolerate equal quantization
• →The approach enables practical deployment of LLM-based autonomous reasoning on resource-constrained onboard computers