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arXiv · 2507.14397

LIMINAL: Exploring The Frontiers of LLM Decode Performance

Michael Davies·Neal Crago·Karthikeyan Sankaralingam·Christos Kozyrakis
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Abstract

The rapid advancement of Large Language Models (LLMs) necessitates a deep understanding of their fundamental performance limits. This paper investigates the limits of LLM inference, focusing on hardware-imposed bottlenecks in auto-regressive decoding. We develop LIMINAL, an analytical performance model that abstracts application requirements and hardware capabilities to systematically explore performance and efficiency across a wide range of current, near-future, and hypothetical hardware. We find LIMINAL is accurate when comparing to LLMs executing on existing hardware, achieving a mean absolute error of $7.6\%$. Our analysis spans from current HBM3 memory technology used in AI accelerators like GPUs and TPUs to systems based on advanced HBM4 and advanced 3D-stacked DRAM technology. We identify five non-negotiable challenges for LLM inference hardware, establishing compute, memory capacity, bandwidth and collective communication as primary barriers to performance. These findings suggest that achieving significant performance gains beyond 10,000 tokens-per-second will require not just hardware evolution but also fundamental algorithmic advances.

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Michael Davies, Neal Crago, Karthikeyan Sankaralingam, Christos Kozyrakis. 2025-07-18. LIMINAL: Exploring The Frontiers of LLM Decode Performance. https://arxiv.org/abs/2507.14397

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