Deployments

New to deployments? Start with our Deployments Quickstart to deploy and query your first model in minutes, then return here to learn about configuration options.

On-demand deployments give you dedicated GPUs for your models, providing several advantages over serverless:

Better performance – Lower latency, higher throughput, and predictable performance unaffected by other users
No hard rate limits – Only limited by your deployment’s capacity
Cost-effective at scale – Cheaper under high utilization. Unlike serverless models (billed per token), on-demand deployments are billed by GPU-second.
Broader model selection – Access models not available on serverless
Custom models – Upload your own models (for supported architectures) from Hugging Face or elsewhere

Need higher GPU quotas or want to reserve capacity? Contact us.

Creating & querying deployments

Create a deployment:

# This command returns your accounts/<ACCOUNT_ID>/deployments/<DEPLOYMENT_ID> - save it for querying
firectl deployment create accounts/fireworks/models/<MODEL_NAME> \
  --deployment-shape default \
  --wait

--deployment-shape default has Fireworks pick a validated shape for the model — see Letting Fireworks pick a shape. To choose a shape yourself, run firectl deployment-shape-version match --model <model-id> first and pass a shape’s name to --deployment-shape.

Deployment placement (--region) must be set at creation time and cannot be changed in place.If you do not specify --region, the deployment is pinned to a single datacenter at creation time and will not be automatically migrated later.For production workloads that need geographic availability or capacity failover, always set --region explicitly:

firectl deployment create accounts/fireworks/models/<MODEL_NAME> \
  --deployment-shape default \
  --region GLOBAL   # recommended default
firectl deployment create accounts/fireworks/models/<MODEL_NAME> \
  --deployment-shape default \
  --region US
firectl deployment create accounts/fireworks/models/<MODEL_NAME> \
  --deployment-shape default \
  --region EUROPE
firectl deployment create accounts/fireworks/models/<MODEL_NAME> \
  --deployment-shape default \
  --region APAC

Only GLOBAL is available by default. Quota for US, EUROPE, APAC, and single regions must be granted by Fireworks — contact sales@fireworks.ai. Deploying with a region you have no quota for is rejected at creation. See Regions for details.

Check current placement

firectl deployment get <DEPLOYMENT_ID>

The deployment metadata shows where the deployment is currently allowed to schedule replicas (placement / region configuration).

Change placement

There is no supported command to change region placement on an existing deployment. To change placement, recreate the deployment:

# 1. Create replacement with correct region
firectl deployment create accounts/fireworks/models/<MODEL_NAME> \
  --deployment-shape default \
  --region GLOBAL \
  --min-replica-count 1

# 2. Verify it's healthy, then point your app at the new endpoint

# 3. Delete old deployment
firectl deployment delete <OLD_DEPLOYMENT_ID>

See Regions for mega-regions and hardware availability. See Deployment shapes below to optimize for speed, throughput, or cost. Query your deployment: After creating a deployment, query it using this format:

accounts/<ACCOUNT_ID>/deployments/<DEPLOYMENT_ID>

You can find your deployment name anytime with firectl deployment list and firectl deployment get <DEPLOYMENT_ID>.

Example:

accounts/alice/deployments/12345678

Code examples

Python (Fireworks SDK)
Python (OpenAI SDK)
JavaScript
curl

from fireworks import Fireworks

client = Fireworks()

response = client.chat.completions.create(
  model="accounts/fireworks/models/gpt-oss-120b#<DEPLOYMENT_NAME>",
  messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}]
)

print(response.choices[0].message.content)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("FIREWORKS_API_KEY"),
    base_url="https://api.fireworks.ai/inference/v1"
)

response = client.chat.completions.create(
    model="accounts/<ACCOUNT_ID>/deployments/<DEPLOYMENT_ID>",
    messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}]
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: "https://api.fireworks.ai/inference/v1",
});

const response = await client.chat.completions.create({
  model: "accounts/<ACCOUNT_ID>/deployments/<DEPLOYMENT_ID>",
  messages: [
    {
      role: "user",
      content: "Explain quantum computing in simple terms",
    },
  ],
});

console.log(response.choices[0].message.content);

curl https://api.fireworks.ai/inference/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $FIREWORKS_API_KEY" \
  -d '{
    "model": "accounts/<ACCOUNT_ID>/deployments/<DEPLOYMENT_ID>",
    "messages": [
      {
        "role": "user",
        "content": "Explain quantum computing in simple terms"
      }
    ]
  }'

Deployment status states

Deployment states from the Gateway API spec:

CREATING - still being created
READY - ready to be used
UPDATING - in-progress updates happening
DELETING - being deleted
DELETED - soft-deleted
FAILED - creation failed (see status for details)

UI-only states are display labels derived from deployment fields:

Inactive: state == READY && max_replica_count == 0 && ready_replica_count == 0
Scaled to 0: state == READY && min_replica_count == 0 && max_replica_count > 0 && desired_replica_count == 0 && ready_replica_count == 0

These are display labels computed from deployment fields; they are not new backend Deployment.State enum values.

Deployment shapes

Deployment shapes are the primary way to configure deployments. They’re pre-configured templates optimized for speed, cost, or efficiency, including hardware, quantization, and other performance factors. Every shape is validated by Fireworks, so a deployment created from a shape uses a known-good configuration of GPU count, precision, and serving parameters.

Do not create deployments without a shape. Always pass --deployment-shape — a concrete shape, or default to have Fireworks pick one. Deployments created without a shape skip these validation checks — mistakes like a GPU count that can’t fit the model surface only at creation, where they cause failures. Most failed deployment creations on Fireworks are deployments without a shape. Enforcement is coming soon: shapeless creation will then require an explicit opt-in — see Explicitly creating a deployment without a shape. If no shape fits your workload, contact us and we’ll help you find or add one.

Over the API, deploymentShape also accepts the special value default: the server picks a validated shape for the model for you — pass --deployment-shape default with firectl. For details, see Letting Fireworks pick a shape.

Fast – Low latency for interactive workloads
Throughput – Cost-per-token at scale for high-volume workloads
Minimal – Lowest cost for testing or light workloads

Usage:

# Let Fireworks pick a validated shape
firectl deployment create accounts/fireworks/models/deepseek-v3 --deployment-shape default

# Or match available shapes (the deployable set for your account) and choose one
firectl deployment-shape-version match --model <model-id>

# Create with a shape (shorthand)
firectl deployment create accounts/fireworks/models/deepseek-v3 --deployment-shape throughput

match reports the deployable set. Shapes your account cannot deploy on are dropped, exact base-model matches are preferred over same-model-type and parameter-count siblings, and LoRA addon and live-merge models are matched against their base model. Over the API, the equivalent is Match Deployment Shape Versions. To see every validated shape matching a base model instead (including ones your account cannot use), use firectl deployment-shape-version list --base-model <model-id> — base models only, it fails on LoRA addon and live-merge models. match does not take addons into account. For a deployment with --enable-addons, use the Match Deployment Shape Versions API with enableAddons set.

# Create with full shape ID
firectl deployment create accounts/fireworks/models/llama-v3p3-70b-instruct \
  --deployment-shape accounts/fireworks/deploymentShapes/llama-v3p3-70b-instruct-fast

# View shape details
firectl deployment-shape-version get <full-deployment-shape-version-id>

Letting Fireworks pick a shape

Every client surface accepts a special value that has the server pick a validated shape for the model and apply it, the same as passing that shape explicitly:

firectl
REST API
Python SDK

firectl deployment create accounts/fireworks/models/gpt-oss-120b \
  --deployment-shape default

Pass "default" as deploymentShape:

curl -X POST "https://api.fireworks.ai/v1/accounts/YOUR_ACCOUNT_ID/deployments" \
  -H "Authorization: Bearer $FIREWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "baseModel": "accounts/fireworks/models/gpt-oss-120b",
    "deploymentShape": "default"
  }'

from fireworks import Fireworks

client = Fireworks()
client.deployments.create(
    account_id="YOUR_ACCOUNT_ID",
    base_model="accounts/fireworks/models/gpt-oss-120b",
    deployment_shape="default",
)

The pick never silently overrides fields you set. If every compatible shape conflicts with fields in your request (for example an acceleratorType no shape validates), the request fails with an error naming the conflicting fields, the compatible shapes, and how to create the deployment without a shape. If no shape is compatible with the model at all, the request also fails — in that case, contact us and we’ll help you find or add one.

Explicitly creating a deployment without a shape (advanced users only)

Every surface accepts an explicit opt-out that creates the deployment without a shape. Setting it together with a shape is an error — a deployment is either shaped or explicitly shapeless.

firectl
REST API
Python SDK

firectl deployment create accounts/fireworks/models/gpt-oss-120b \
  --accept-shapeless-risk

Pass acceptShapelessRisk=true as a query parameter:

curl -X POST "https://api.fireworks.ai/v1/accounts/YOUR_ACCOUNT_ID/deployments?acceptShapelessRisk=true" \
  -H "Authorization: Bearer $FIREWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "baseModel": "accounts/fireworks/models/gpt-oss-120b"
  }'

from fireworks import Fireworks

client = Fireworks()
client.deployments.create(
    account_id="YOUR_ACCOUNT_ID",
    base_model="accounts/fireworks/models/gpt-oss-120b",
    accept_shapeless_risk=True,
)

Deployments created this way skip shape validation and are far more likely to fail at creation — most failed deployment creations on Fireworks are shapeless deployments. Only use the opt-out when you have a specific reason no shape covers, and contact us so we can help. Enforcement is coming soon: shapeless creation will then require this flag, so use a shape (or default) now unless you deliberately need this path.

Need even better performance with tailored optimizations? Contact our team.

Managing & configuring deployments

Basic management

# List all deployments
firectl deployment list

# Check deployment status
firectl deployment get <DEPLOYMENT_ID>

# Delete a deployment
firectl deployment delete <DEPLOYMENT_ID>

Use deployment tags to attach customer-defined metadata such as an environment, team, or workload.

By default, deployments scale to zero if unused for 1 hour. Deployments with min replicas set to 0 are automatically deleted after 7 days of no traffic.

When a deployment is scaled to zero, requests return a 503 error immediately while the deployment scales up. Your application should implement retry logic to handle this. See Scaling from zero behavior for implementation details.

GPU hardware

Do not create deployments without a shape. If a deployment shape fits your workload, use it — this section only applies when no shape fits. Creating a deployment without a shape (not passing --deployment-shape) skips validation: most failed deployment creations on Fireworks are deployments without a shape, and shapeless creation will soon require an explicit opt-in. If you need a configuration no shape covers, contact us and we’ll help you find or add one.

Choose GPU type with --accelerator-type:

NVIDIA_A100_80GB
NVIDIA_H100_80GB
NVIDIA_H200_141GB
NVIDIA_B200_180GB
NVIDIA_B300_288GB
AMD_MI325X_256GB
AMD_MI350X_288GB

GPU availability varies by region. See the Create Deployment API reference for the authoritative list of supported accelerator types.

Not every model runs on every GPU. Each model is validated only on specific accelerator type, GPU count, and precision combinations — many models support just one or two entries from the list above. To see what a model supports, match its deployment shapes:

firectl deployment-shape-version match --model accounts/fireworks/models/<MODEL_NAME>

The GPUS, ACCELERATOR, and PRECISION columns are the validated combinations; the model’s page in the Fireworks console shows the same options under dedicated deployments. Setting --accelerator-type or --accelerator-count to a combination no shape validates will typically fail at creation — often with a generic Internal error occurred message that does not name the hardware mismatch. If you need hardware no shape covers, contact us.

Autoscaling

Control replica counts, scale timing, and load targets for your deployment. See the Autoscaling guide for configuration options.

Multiple GPUs per replica

Use multiple GPUs to improve latency and throughput. First check whether a multi-GPU deployment shape already exists for your model:

firectl deployment-shape-version match --model <MODEL_NAME>

If no multi-GPU shape fits, start from the closest shape and override the GPU count with --accelerator-count — see the warning under GPU hardware for why omitting --deployment-shape entirely should be a last resort:

firectl deployment create <MODEL_NAME> \
  --deployment-shape <SHAPE_NAME> \
  --accelerator-count 2

More GPUs = faster generation. Note that scaling is sub-linear (2x GPUs ≠ 2x performance).

Preemptible deployments (eval / batch)

For the training eval workflow, see Evaluating Trained Models. Preemptible deployments let you run evaluation and batch workloads without holding dedicated on-demand capacity. Passing --preemptible at creation opts the deployment into borrowing idle reserved GPUs instead of reserving GPUs exclusively for you, so you do not pay to hold dedicated capacity for an eval that only runs occasionally. Because the capacity is borrowed, it can be reclaimed (preempted) at any time and is not guaranteed. In practice a preemptible deployment usually stays up long enough to run an eval end to end, but you must treat disappearance as a normal outcome, not an error.

Eval and batch workloads only. A preemptible deployment can be preempted mid-request and disappear with no warning. Never point production or latency-sensitive traffic at a preemptible deployment.

Guarantees vs. risks

	Preemptible (`--preemptible`)	Standard on-demand
Capacity model	Borrows idle reserved GPUs	Reserves dedicated GPUs for you
Cost	No charge to hold dedicated capacity	Billed by GPU-second while replicas are active
Can be reclaimed mid-request?	Yes, at any time, without warning	No
Availability guarantee	None (treat as best-effort)	Held for you once scheduled
Safe for production / latency-sensitive traffic?	No	Yes
Intended for	Evaluation, batch, one-off runs	Production and interactive serving

No unique availability risk. Preemptible does not make capacity harder to get. If there is no capacity for a preemptible deployment, there is no capacity for an on-demand one either. The only difference is that a preemptible deployment can also be taken back after it starts.

Requirements

firectl >= 1.7.26. --preemptible is a newer flag and is silently ignored on older builds (no error, and the deployment is created as standard on-demand). Check with firectl version and upgrade if you are below this version.

Create a preemptible deployment

firectl deployment create accounts/fireworks/models/<MODEL> \
  -a <ACCOUNT_ID> \
  --deployment-id <NAME> --display-name <NAME> \
  --deployment-shape default \
  --min-replica-count 1 --max-replica-count 1 \
  --preemptible --wait

--wait blocks until the deployment is ready (1 hour default; tune with --wait-timeout).

--preemptible is immutable. It is set at creation time and cannot be toggled on or off later. To change it, delete the deployment and recreate it.

Check status and then send eval requests as you would to any deployment:

firectl deployment get <NAME> -a <ACCOUNT_ID>

When the eval is done, delete the deployment to release the borrowed capacity:

firectl deployment delete <NAME> -a <ACCOUNT_ID>

Advanced

Speculative decoding - Speed up text generation using draft models or n-gram speculation
Quantization - Reduce model precision (e.g., FP16 to FP8) to improve speeds and reduce costs by 30-50%
Performance benchmarking - Measure and optimize your deployment’s performance with load testing
Managing default deployments - Control which deployment handles queries when using just the model name
Publishing deployments - Make your deployment accessible to other Fireworks users

Next steps

Autoscaling

Configure autoscaling for optimal cost and performance

Upload custom models

Deploy your own models from Hugging Face

Quantization

Reduce costs with model quantization

Regions

Choose deployment regions for optimal latency

Reserved capacity

Purchase reserved GPUs for guaranteed capacity

Training

Train models for your specific use case

Get Started

Serverless Inference

Models & Inference

Training

Managed Training

Training API

Fire Pass

Fireworks Nexus

Administration

Security & Compliance

Integrations

Reference

Deployments

Creating & querying deployments

Check current placement

Change placement

Code examples

Deployment status states

Deployment shapes

Letting Fireworks pick a shape

Explicitly creating a deployment without a shape (advanced users only)

Managing & configuring deployments

Basic management

GPU hardware

Autoscaling

Multiple GPUs per replica

Preemptible deployments (eval / batch)

Guarantees vs. risks

Requirements

Create a preemptible deployment

Advanced

Next steps

Autoscaling

Upload custom models

Quantization

Regions

Reserved capacity

Training

​Creating & querying deployments

​Check current placement

​Change placement

​Code examples

​Deployment status states

​Deployment shapes

​Letting Fireworks pick a shape

​Explicitly creating a deployment without a shape (advanced users only)

​Managing & configuring deployments

​Basic management

​GPU hardware

​Autoscaling

​Multiple GPUs per replica

​Preemptible deployments (eval / batch)

​Guarantees vs. risks

​Requirements

​Create a preemptible deployment

​Advanced

​Next steps

Autoscaling

Upload custom models

Quantization

Regions

Reserved capacity

Training

Creating & querying deployments

Check current placement

Change placement

Code examples

Deployment status states

Deployment shapes

Letting Fireworks pick a shape

Explicitly creating a deployment without a shape (advanced users only)

Managing & configuring deployments

Basic management

GPU hardware

Autoscaling

Multiple GPUs per replica

Preemptible deployments (eval / batch)

Guarantees vs. risks

Requirements

Create a preemptible deployment

Advanced

Next steps