Latency

Increasing batch size to improve throughput typically sacrifices per-request latency

Under a linear conversation structure, token consumption is cumulative—every request requires retransmitting the entire history, increasing inference latency and driving up API call costs.

Latency refers to the time required for a single request from being issued to completion.

The pre-built movement library approach can reduce latency from 15 seconds to 2 seconds and save AI computing time and token costs, while ensuring that instructional videos and movement key points are 100% accurate and consistent.

The additional latency introduced by the aggregation layer mainly comes from three aspects: protocol conversion and routing decisions in the proxy process, longer network paths for cross-vendor calls, and the extra round trips added by the failure retry mechanism when rate-limited.

The impact of the US node launch on end users centers mainly on three aspects: latency and routing improvements, redundancy and disaster recovery, and compliance and data residency.

When evaluating an inference engine, throughput and latency must be distinguished: throughput usually refers to the total number of tokens generated by the whole system per unit of time, while latency focuses on the response speed of a single request, divided into first-token latency and per-token generation interval; a larger batch can improve throughput but may raise the latency of a single request.

The research presents four typical trade-offs between privacy and performance: latency vs privacy, model accuracy vs differential privacy, scalability vs federated learning, and transparency vs proprietary SDKs.

Latency is one of the most sensitive probes in distributed systems, because connection pool exhaustion, downstream dependency jitter, and cold starts all leave traces in P99 latency first, often before the error rate spikes.

A good P50 median latency does not mean that P99 tail latency is acceptable; for real-time decision scenarios, tail latency is often the true bottleneck.

When the routing results for a certain type of request are highly concentrated, the routing layer can be removed and the model bound directly to reduce latency and scheduling costs; if routing results remain consistently dispersed, the router's dynamic decision-making should be retained.

Pursuing extreme throughput often comes at the cost of per-request latency, as throughput and latency metrics constrain each other.

Pursuing extremely low latency means decreased GPU utilization and rising per-inference costs.

In chip architecture design, throughput and latency are typically a pair of metrics that require trade-offs.

路由决策若用小模型可能判断不准，若用大模型则路由开销会抵消节省，需要在准确性、延迟、成本之间权衡