litellm/docs/my-website/docs/proxy/load_balancing.md

Multiple Instances

Load balance multiple instances of the same model

The proxy will handle routing requests (using LiteLLM's Router). Set rpm in the config if you want maximize throughput

:::info

For more details on routing strategies / params, see Routing

:::

Load Balancing using multiple litellm instances (Kubernetes, Auto Scaling)

LiteLLM Proxy supports sharing rpm/tpm shared across multiple litellm instances, pass redis_host, redis_password and redis_port to enable this. (LiteLLM will use Redis to track rpm/tpm usage )

Example config

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/<your-deployment-name>
      api_base: <your-azure-endpoint>
      api_key: <your-azure-api-key>
      rpm: 6      # Rate limit for this deployment: in requests per minute (rpm)
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/gpt-turbo-small-ca
      api_base: https://my-endpoint-canada-berri992.openai.azure.com/
      api_key: <your-azure-api-key>
      rpm: 6
router_settings:
  redis_host: <your redis host>
  redis_password: <your redis password>
  redis_port: 1992

Router settings on config - routing_strategy, model_group_alias

litellm.Router() settings can be set under router_settings. You can set model_group_alias, routing_strategy, num_retries,timeout . See all Router supported params here

Example config with router_settings

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/<your-deployment-name>
      api_base: <your-azure-endpoint>
      api_key: <your-azure-api-key>
      rpm: 6      # Rate limit for this deployment: in requests per minute (rpm)
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/gpt-turbo-small-ca
      api_base: https://my-endpoint-canada-berri992.openai.azure.com/
      api_key: <your-azure-api-key>
      rpm: 6
router_settings:
  model_group_alias: {"gpt-4": "gpt-3.5-turbo"} # all requests with `gpt-4` will be routed to models with `gpt-3.5-turbo`
  routing_strategy: least-busy                  # Literal["simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing"]
  num_retries: 2
  timeout: 30                                  # 30 seconds
  redis_host: <your redis host>
  redis_password: <your redis password>
  redis_port: 1992