Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The paper introduces BEA, a fine‑grained estimator that predicts the time‑to‑first‑token (TTFT) for language model queries by replaying token‑batch formation based on current workload snapshots. BEA achieves about 5% error with 0.1 ms prediction time, enabling the latency‑aware router BEAR to balance accuracy, cost, and latency across heterogeneous LLM pools. BEAR outperforms seven baselines, improving utility of on‑target responses by 63% on average and by 30.5% at each arrival rate.
arXiv:2607. 18253v1 Announce Type: new Abstract: Modern language query routers improve inference efficiency by assigning each query to a model that balances response quality and monetary cost.
arXiv:2608. 13571v1 Announce Type: cross Abstract: When a language model fails to answer a query on the first attempt, an agentic system retries, consuming additional tokens each time.
TokenRouter is a new serving system designed for token‑level routing of large language models (LLMs). It adopts a request‑centric programming model and launches a dedicated subserver for each LLM, using delayed‑batching schedulers whose hyperparameters are derived from a throughput model. Across various routing algorithms, workloads, and model pairs, TokenRouter delivers 2.01‑64.15× higher decoding throughput than existing systems, markedly improving the efficiency of token‑level LLM inference.
arXiv:2608. 06557v1 Announce Type: cross Abstract: The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges to long, compute-heavy requests.
arXiv:2609.38006v1 Announce Type: new Abstract: Running a language model locally offers advantages in privacy, latency, and cost, but local hardware fits only small models, which are less capable tha...