Fireworks allows you to create on-demand, dedicated deployments that are reserved for your own use. This has several advantages over the shared deployment Fireworks used for its serverless models:

• Predictable performance unaffected by load caused by other users
• No hard rate limits - but subject to the maximum load capacity of the deployment
• Cheaper under high utilization
• Access to larger selection of models not available via our serverless models
• Custom base models from Hugging Face files

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Quickstart

firectl create deployment accounts/fireworks/models/<MODEL_ID> --wait

firectl create deployment <MODEL_ID>

firectl get deployment <DEPLOYMENT_ID>

curl \
  --header 'Authorization: Bearer <FIREWORKS_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "accounts/fireworks/models/<MODEL_ID>#accounts/<ACCOUNT_ID>/deployments/<DEPLOYMENT_ID>",
    "prompt": "Say this is a test"
  }' \
  --url https://api.fireworks.ai/inference/v1/completions

firectl delete deployment <DEPLOYMENT_ID>

Notes:

• Make sure you include the #<DEPLOYMENT_NAME> in the model identifier when querying a specific deployment.
• If you are unsure about the model identifier format, refer to the Model Identifiers section for more details and alternatives.

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Deployment options

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Replica count (horizontal scaling)

The number of replicas (horizontal scaling) is specified by passing the --min-replica-count and --max-replica-count flags. Increasing the number of replicas will increase the maximum QPS the deployment can support. The deployment will automatically scale based on server load.

The default value for --min-replica-count is 0. Setting --min-replica-count to 0 enables the deployment to auto-scale to 0 if a deployment is unused (i.e. no inference requests) for a specified “scale-to-zero” time window. While the deployment is scaled to 0, you will not pay for any GPU utilization.

The default value for --max-replica-count is 1 if --min-replica-count=0, or the value of --min-replica-count otherwise.

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Customizing autoscaling behavior

You can customize certain aspects of the deployment’s autoscaling behavior by setting the following flags:

• --scale-up-window The duration the autoscaler will wait before scaling up a deployment after observing increased load. Default is 30s.

• --scale-down-window The duration the autoscaler will wait before scaling down a deployment after observing decreased load. Default is 10m.

• --scale-to-zero-window The duration after which there are no requests that the deployment will be scaled down to zero replicas. This is ignored if --min-replica-count is greater than 0. Default is 1h. The minimum is 5m.

  There will be a cold-start latency (up to a few minutes) for requests made while the deployment is scaling from 0 to 1 replicas.
  A deployment with --min-replica-count set to 0 will be automatically deleted if it receives no traffic for 7 days.

Refer to time.ParseDuration for valid syntax for the duration string.

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Multiple GPUs (vertical scaling)

The number of GPUs used per replica is specified by passing the --accelerator-count flag. Increasing the accelerator count will increase the generation speed, time-to-first-token, and maximum QPS for your deployment, however the scaling is sub-linear. The default value for most models is 1 but may be higher for larger models that require sharding.

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Choosing hardware type

By default, a deployment will use NVIDIA A100 80 GB GPUs. You can also deploy using NVIDIA H100 80 GB or AMD MI300X GPUs by passing the --accelerator-type flag. Valid values for --accelerator-type are:

• NVIDIA_H100_80GB
• NVIDIA_A100_80GB
• AMD_MI300X_192GB - Note that MoE-based models like DeepSeek Coder and Mixtral are currently not supported on MI300X

For advice on choosing a hardware type, see this FAQ

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Quantization

By default, models on dedicated deployments are served using 16-bit floating-point (FP16) precision. Quantization reduces the number of bits used to serve the model, improving performance and reducing cost to serve. However, this can changes model numerics which may introduce small changes to the output.

In order to deploy a base model using quantization, it must be prepared first. See our Quantization guide for details.

To create a deployment using a quantized model, pass the --precision flag with the desired precision.

firectl create deployment <MODEL_NAME> \
    --accelerator-type="NVIDIA_H100_80GB" \
    --precision="FP8"

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Optimizing your deployments for long context

By default, a balanced deployment will be created using the hardware resources you specify. Higher performance can be achieved for long-prompt length (>~3000 tokens) workloads by passing the --long-prompt flag.

To update a deployment to disable this option, pass --long-prompt=false.

Additional optimization options are available through our enterprise plan.

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Deploying PEFT addons

By default, PEFT addons are disabled for deployments. To enable addons, pass the --enable-addons flag:

See Uploading a custom model for instructions on how to upload custom PEFT addons. To deploy a PEFT addon to a on-demand deployment, pass the --deployment-id flag to firectl deploy. For example:

firectl deploy <MODEL_ID> --deployment-id <DEPLOYMENT_ID>

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Pricing

On-demand deployments are billed by GPU-second. Consult our pricing page for details.