arXiv:2605.30571v1 Announce Type: cross Abstract: Physical AI systems, including robots, autonomous vehicles, embodied agents and edge copilots, often run a different inference workload from cloud LLM serving: single-stream, batch-1 autoregressive decode, where one robot, camera feed or user session waits on the next token. This workload is usually described as memory-bandwidth-bound. Each decode step streams model weights and the active KV cache, so latency should scale with peak HBM bandwidth. We show that this account is true but incomplete. We measure batch-1 decode for three 7 to 8B-class
The proliferation of physical AI systems like robots and autonomous vehicles is driving a re-evaluation of LLM inference architectures.
Understanding the true bottlenecks in batch-1 LLM inference is crucial for optimizing hardware and software design for real-time edge AI applications, impacting their scalability and performance.
This research refines the understanding of memory-bandwidth limitations in edge AI, suggesting that architectural adjustments are needed beyond simply increasing HBM bandwidth.
- · Edge AI hardware developers
- · Robotics companies
- · Autonomous vehicle manufacturers
- · Specialized AI chip designers
- · General-purpose cloud LLM hardware
- · Developers solely focused on HBM bandwidth improvements
Optimized hardware designs will emerge specifically for real-time, batch-1 LLM inference.
This specialization could lead to a divergence in AI chip development for edge versus cloud applications.
Enhanced efficiency in physical AI systems might accelerate their deployment and capabilities in diverse industries.
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Read at arXiv cs.AI