Latency Analysis and Optimization of Alpamayo 1 via Efficient Trajectory Generation

Recent advances in end-to-end autonomous driving leverage deep neural networks to directly map sensor inputs to control actions, exemplified by systems like Waymo and Tesla Autopilot. These models have evolved from modular pipelines to integrated architectures, with reasoning-based approaches gaining attention for their interpretability. Vision-language models (VLMs) such as Qwen3-VL have been integrated into driving systems, enabling reasoning over multimodal inputs. Diffusion models have been adopted for action generation, improving trajectory realism. Despite these advances, the computational cost remains prohibitive for real-time deployment, especially when multiple trajectories are generated for safety and robustness. Prior work has focused on model accuracy but often neglects inference efficiency, which is critical for practical applications.

The core challenge lies in balancing trajectory diversity and inference speed. Multi-reasoning architectures, while enhancing behavioral variability, lead to linear increases in latency proportional to the number of trajectories, making real-time operation infeasible. Diffusion-based action generation, involving iterative denoising steps, further exacerbates latency due to repeated GPU kernel launches and memory operations. Existing solutions either simplify models at the expense of diversity or accept high latency, limiting their deployment in safety-critical autonomous vehicles. Therefore, a fundamental redesign of the inference pipeline is necessary to achieve both high diversity and low latency.

The primary innovation is transforming the multi-reasoning architecture into a single-reasoning framework, sharing a common reasoning sequence across all trajectories. This reduces redundant computation and input duplication. To compensate for reduced reasoning variability, stochastic initialization of action sequences maintains diversity. Additionally, the use of static KV cache preallocation eliminates dynamic memory reallocation during diffusion, while CUDA graph capture consolidates GPU kernel calls, significantly reducing overhead. These combined strategies form a holistic approach that integrates system-level architecture changes with hardware-aware optimizations, setting a new standard for efficient reasoning models.

• �� Conduct detailed profiling of Alpamayo inference pipeline to identify latency bottlenecks.
• �� Replace multi-reasoning with a single reasoning sequence, reusing KV caches across trajectories.
• �� Offline profile KV cache growth to preallocate static buffers, avoiding dynamic memory operations.
• �� Implement CUDA graph capture to record and replay GPU kernel sequences, minimizing launch overhead.
• �� Validate the optimized pipeline through extensive experiments on NVIDIA datasets and simulation environments, comparing inference latency, trajectory diversity, and prediction accuracy.
• �� Analyze the impact of the number of diffusion steps and reasoning tokens on overall latency, ensuring robustness across scenarios.

Experiments utilize NVIDIA DGX Spark hardware, with NVIDIA Physical AI Dataset for real-world scenarios and AlpaSim for simulation. Baselines include original multi-reasoning Alpamayo and simplified single-reasoning variants. Metrics include inference latency, trajectory diversity (measured by entropy), and prediction accuracy (ADE, FDE). Hyperparameters such as number of diffusion steps, reasoning tokens, and trajectories are varied systematically. Ablation studies isolate the effects of KV cache preallocation and CUDA graph capture. Results demonstrate that optimized models maintain comparable accuracy and diversity while significantly reducing latency, validating the effectiveness of the proposed methods.

The optimized single-reasoning architecture reduces inference latency from ~4 seconds to ~1.2 seconds, a 69.23% improvement. Trajectory diversity remains stable, with entropy scores comparable to baseline multi-reasoning models. GPU kernel call reduction and static KV buffers cut diffusion sampling time by over 50%. The latency scales sub-linearly with the number of trajectories, enabling real-time multi-path prediction. These results confirm that the combined system and runtime optimizations effectively address the core bottlenecks, making the model suitable for practical autonomous driving scenarios.