On-demand deployments

Fireworks allows you to create on-demand deployments of models that are reserved for your own use and billed by the GPU-second. This has several advantages over the shared deployment architecture you get when using Fireworks’ 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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Installing the CLI

To create and manage on-demand deployments, use our CLI tool firectl. Install the tool by following the installation guide. Verify that your installation is working and you are logged into the correct account by running:

firectl whoami

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Creating an on-demand deployment

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

firectl create deployment accounts/<YOUR_ACCOUNT_ID>/models/<CUSTOM_MODEL_ID> --wait

firectl get deployment <DEPLOYMENT_ID>

firectl list deployments

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

firectl delete deployment <DEPLOYMENT_ID>

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Checking whether a model is deployed

You can check if a particular model has been deployed by running:

firectl get model accounts/<ACCOUNT_ID>/models/<MODEL_ID>

If successful, there will be an entry with State: DEPLOYED in the Deployed Model Refs section.

This works both for models provided by Fireworks and for custom models you have uploaded to your account. For example, if you want to confirm if “Qwen2.5 7B Instruct” is deployed, you can run:

firectl get model accounts/fireworks/models/qwen2p5-7b-instruct

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Managing default deployments

Since a model may be deployed to multiple deployments, querying by model name will route to the “default” deployed model. You can see which deployed model entry is marked with Default: true using:

firectl get model <MODEL_ID>

This will show the Deployed Model Refs section with the Default: true entry.

Deployed Model Refs:
  [{
    Name: accounts/<ACCOUNT_ID>/deployedModels/<DEPLOYED_MODEL_ID_1>
    Deployment: accounts/<ACCOUNT_ID>/deployments/<DEPLOYMENT_ID_1>
    State: DEPLOYED
    Default: true
  },
  {
    Name: accounts/<ACCOUNT_ID>/deployedModels/<DEPLOYED_MODEL_ID_2>
    Deployment: accounts/<ACCOUNT_ID>/deployments/<DEPLOYMENT_ID_2>
    State: DEPLOYED
  },
]

To update the default deployed model, note the Name of the deployed model reference above. Then run:

firectl update deployed-model <DEPLOYED_MODEL_ID_2> --default

To delete a default deployment you must delete all other deployments for the same model first, or designate a different deployed model as the default as described above. This is to ensure that querying by model name will always route to an unambiguous default deployment as long as deployments for the model exist.

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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.

firectl create deployment <MODEL_NAME> \
  --min-replica-count 2 \
  --max-replica-count 3

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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.

• --load-targets <key>=<value>[,<key>=<value>...] Load target thresholds for scaling the replica count. If not specified, the load target is default with --load-targets default=0.8. If multiple load targets are specified the maximum replica count across all of them is used.

  • default=<Fraction> - A general default value for 0 to 1 load targets. Default is default=0.8.
  • tokens_generated_per_second=<Integer> - The desired tokens generated per second per replica.
  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.

firectl create deployment <MODEL_NAME> --accelerator-count 2

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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, NVIDIA H200 141GB or AMD MI300X GPUs by passing the --accelerator-type flag. Valid values for --accelerator-type are:

• NVIDIA_A100_80GB
• NVIDIA_H100_80GB
• NVIDIA_H200_141GB
• 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

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

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Model based speculative decoding

Model based speculative decoding allows you to speed up output generation in some cases, by using a smaller model to assist the larger model in generation.

We offer the following settings that can be set as flags in firectl, our CLI tool:

You can use the following draft models directly:

Here’s an example of deploying Llama 3.3 70B with a draft model:

firectl create deployment accounts/fireworks/models/llama-v3p1-8b-instruct \
  --accelerator-type="NVIDIA_H100_80GB" \
  --draft-model="accounts/fireworks/models/llama-v3p2-1b-instruct" \
  --draft-token-count=4

In most cases, speculative decoding does not change the quality of the output generated (mathematically, outputs are unchanged, but there might be numerical differences, especially at higher temperatures). If speculation is used on the deployment and you want to verify the output is unchanged, you can set disable_speculation=True in the inference API call - in this case, the draft model is still called but its output are not used, so performance will be impacted.

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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.

firectl create deployment <MODEL_NAME> --accelerator-count=2 --long-prompt

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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Publishing a deployed model

By default, models can only be queried by the account that owns them. To make a deployment public so anyone with a valid Fireworks API key can query it, update the deployed model with the --public flag.

firectl list deployed-models --filter 'model="<MODEL_NAME>" AND deployment="<DEPLOYMENT_NAME>"'

firectl update deployed-model <DEPLOYED_MODEL_ID> --public

firectl update deployed-model <DEPLOYED_MODEL_ID> --public=false