The UpdateDeploymentConfig class defines the parameters used for updating private serverless deployments. The parameters and their definitions match DeploymentConfig, except for accelerator, which cannot be changed via an update.

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Parameters

ParameterTypeRequiredDefaultDescription
base_modelstringYesThe base model to deploy. Can be a short name from our models list or Hugging Face model path
cooldown_timeintegerNo3600Time in seconds before scaling down idle replicas. Minimum is 10 minutes (600 seconds)
hf_tokenstringNoHugging Face token for private models
min_replicasintegerNo0Minimum number of replicas
max_replicasintegerNo1Maximum number of replicas
scale_up_thresholdintegerNo1Number of queued requests before scaling up additional replicas
quantizationstringNoQuantization method (none, fp8, bitsandbytes-nf4). Default is based on the model and accelerator.
uses_guaranteed_capacitybooleanNofalseWhether to use guaranteed capacity
max_total_tokensintegerNoMaximum number of tokens per request
lorax_image_tagstringNoTag for the LoRAX image
request_logging_enabledbooleanNofalseWhether to enable request logging
direct_ingressbooleanNofalseCreates a direct endpoint to the LLM, bypassing the Predibase control plane
preloaded_adaptersarray[string]NoList of adapter IDs to preload on deployment initialization
speculatorstringNoSpeculator to use for the deployment (auto, disabled, or adapter ID of a Turbo or Turbo LoRA)
prefix_cachingbooleanNofalseWhether to enable prefix caching
cache_modelbooleanNofalseIf true, caches the HF weights of the model in a private S3 bucket (see details)
custom_argsarray[string]NoCustom arguments to pass to the LoRAX launcher

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Example Usage

from predibase import UpdateDeploymentConfig

# Update scaling configuration
pb.deployments.update(
    deployment_ref="my-qwen3-8b",
    config=UpdateDeploymentConfig(
        min_replicas=1,    # Change to always-on (disable scale-to-zero)
        max_replicas=2,    # Allow scaling to 2 replicas for higher load
        cooldown_time=1800 # Change cooldown time
    )
)

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Additional Pointers

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Model caching

Availability: Model caching is exclusively available to VPC customers. Cached models are stored in a private VPC bucket with read access restricted to the VPC customer only. Behavior:
  • Deployments always load model weights fom the S3 cache, if available.
  • The cacheModel parameter controls whether model weights are written to the cache.
Versioning:
  • By default, weights are cached at the latest model revision
  • To cache a specific revision, specify the base_model parameter with the format: model@revision