arXiv:2511.04791v2 Announce Type: replace Abstract: Modern LLM serving systems must sustain high throughput while meeting strict latency SLOs across two distinct inference phases: compute-intensive prefill and memory-bound decode phases. Existing approaches either (1) aggregate both phases on shared GPUs, leading to interference between prefill and decode phases, which degrades Time-Between-Tokens (TBT); or (2) disaggregate the two phases across GPUs, improving latency but wasting resources through duplicated models and KV cache transfers. We present DuetServe, a unified LLM serving framework
The rapid scaling of LLMs has exposed significant inefficiencies in current serving architectures, driving immediate innovation to optimize resource utilization and meet growing demand for AI inference.
Improving LLM serving efficiency directly impacts the cost and scalability of AI applications, making advanced AI more accessible and economically viable.
This research proposes a new framework, DuetServe, that adaptively manages GPU resources for different LLM inference phases, potentially offering more efficient and less costly LLM deployment.
- · Cloud providers
- · AI developers
- · LLM serving companies
- · GPU manufacturers
- · Inefficient LLM serving solutions
- · Companies with high inference costs
More efficient LLM deployments will reduce operational costs for AI companies and enhance service delivery.
Lower compute costs will accelerate the development and adoption of sophisticated AI models across various industries.
Increased accessibility to advanced AI could democratize AI development, fostering innovation and competition at the application layer.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG