![[2605.30571] Memory-Bound but Not Bandwidth-Limited: The Physical AI Inference Gap in Batch-1 LLM Decode](/static/img/curated/desks/afbytes-tech.w400.webp)
LLM Decode Remains Memory-Bound Despite Bandwidth Advances
The study demonstrates that batch-1 LLM decoding is constrained by memory capacity rather than memory bandwidth. It quantifies the resulting performance gap for physical AI deployments.
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AFBytes briefing
Understanding inference bottlenecks helps data-center operators plan hardware purchases that affect cloud service pricing for businesses and developers.
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- Abstract
- Inference
What to watch next
- Monitor next-generation memory product announcements and their impact on published LLM inference throughput numbers.
UniScale Adaptive Inference Scaling Optimization
The paper presents UniScale as an adaptive framework for unified inference scaling. It jointly optimizes model routing and test-time scaling. The method aims to improve efficiency across varying workloads.