arXiv:2606.03910v1 Announce Type: cross Abstract: Disaggregated LLM inference forces the KV cache to traverse the datacenter network before decoding begins, so transfer time enters directly into the Time to First Token (TTFT) budget. Current schedulers route on compute load and prefix-cache locality alone, ignoring the topological distance and dynamic congestion between prefill and decode instances. We close this gap with a thin operator-to-scheduler interface, the network cost oracle, and we prove that ignoring the network term renders cache-aware-only scheduling arbitrarily suboptimal as con
The increasing scale of LLMs and the adoption of disaggregated architectures necessitate more efficient resource management, making network awareness crucial as compute infrastructure rapidly scales.
Optimizing network-aware decode instance selection can significantly improve LLM inference efficiency and Time to First Token, directly impacting the operational costs and user experience of large language models.
Current scheduling paradigms for LLM inference will evolve to incorporate network topology and congestion, moving beyond solely compute load and cache locality considerations.
- · Hyperscale cloud providers
- · LLM operators
- · Datacenter networking companies
- · AI infrastructure software providers
- · LLM operators with suboptimal network architectures
- · Generic compute-only scheduling solutions
Reduced latency and cost for deploying and scaling large language models.
Increased ability to scale LLM inference to even larger models and user bases, driving further adoption of AI.
Shift in datacenter hardware and software design priorities towards optimizing network performance for AI workloads, potentially stimulating innovation in network fabrics and distributed computing frameworks.
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Read at arXiv cs.AI