Load Balancing
A strategy by which an AI gateway distributes requests across multiple model instances based on weight, latency or cost. It can route simple requests to cheaper small models and complex requests to flagship models.
Timeline (last 90 days)
The creation, status query and result download of stateful jobs (such as AI video generation) must be bound to the same resource instance, otherwise status queries fail because they are routed to other instances.
There is an inherent tension between expert balance and token coverage, and any routing strategy must make a choice within this tension.
In Token-Choice routing, the routing network produces scores for all experts for each token, then selects the Top-K experts to process that token; its inherent risk is imbalanced expert utilization, and common countermeasures include additive bias to adjust routing scores, auxiliary load balancing losses, and expert capacity limits.
An AI gateway can distribute requests across multiple model instances by weight, latency or cost, for example routing 80% of simple requests to a cheap small model and routing complex requests to a flagship model.
Cache affinity pulls requests toward the same batch of replicas, which easily creates hotspots; pure load balancing sacrifices reuse, so the router needs to make a dynamic trade-off between the two.
OmniRoute essentially implements a combination of reverse proxy and load balancing, running locally and listening on a fixed port, translating incoming API requests into the format required by the target provider before forwarding them.
Common load balancing algorithms include round robin, least connections, and consistent hashing.
Simultaneous anomalies across multiple models typically indicate that the problem is not an inference failure of any single model, but rather a condition in deeper shared infrastructure, such as a unified API gateway, load-balancing layer, authentication service, or backend GPU cluster scheduling system.
All Facts (8)
The creation, status query and result download of stateful jobs (such as AI video generation) must be bound to the same resource instance, otherwise status queries fail because they are routed to other instances.
50%UnverifiedThere is an inherent tension between expert balance and token coverage, and any routing strategy must make a choice within this tension.
50%UnverifiedIn Token-Choice routing, the routing network produces scores for all experts for each token, then selects the Top-K experts to process that token; its inherent risk is imbalanced expert utilization, and common countermeasures include additive bias to adjust routing scores, auxiliary load balancing losses, and expert capacity limits.
50%UnverifiedAn AI gateway can distribute requests across multiple model instances by weight, latency or cost, for example routing 80% of simple requests to a cheap small model and routing complex requests to a flagship model.
50%UnverifiedCache affinity pulls requests toward the same batch of replicas, which easily creates hotspots; pure load balancing sacrifices reuse, so the router needs to make a dynamic trade-off between the two.
50%UnverifiedOmniRoute essentially implements a combination of reverse proxy and load balancing, running locally and listening on a fixed port, translating incoming API requests into the format required by the target provider before forwarding them.
50%UnverifiedCommon load balancing algorithms include round robin, least connections, and consistent hashing.
50%UnverifiedSimultaneous anomalies across multiple models typically indicate that the problem is not an inference failure of any single model, but rather a condition in deeper shared infrastructure, such as a unified API gateway, load-balancing layer, authentication service, or backend GPU cluster scheduling system.
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