arXiv:2605.09735v2 Announce Type: replace-cross Abstract: Static-graph LLM decoders provide predictable launches, fixed tensor shapes, and low submission overhead, but online decoding exposes highly irregular KV-cache behavior: request lengths differ, EOS events arrive asynchronously, and logical histories fragment over time. Dynamic runtimes recover flexibility through paged KV management and step-level scheduling, while static-graph executors often over-reserve memory and suffer burst-time latency outliers. This paper studies whether much of this variability can be absorbed below a fixed dec
The increasing scale and complexity of LLMs are pushing the limits of current serving infrastructure, driving innovation in efficient KV-cache management for predictable performance.
Improving the efficiency and predictability of LLM serving infrastructure is crucial for scaling AI applications, reducing operational costs, and supporting the widespread deployment of advanced AI models.
This research suggests a more robust and efficient method for managing KV-caches in static-graph LLM decoders, potentially leading to lower latency variability and better resource utilization in AI inference.
- · Cloud AI providers
- · LLM developers
- · AI-as-a-service companies
- · Hyperscalers
- · Legacy AI inference hardware
- · Inefficient LLM serving platforms
More cost-effective and performant deployment of large language models.
Accelerated development and adoption of AI applications due to improved infrastructure capabilities.
Increased competition and innovation in the AI inference and serving market, potentially leading to specialized hardware or software solutions.
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Read at arXiv cs.AI