> ## Documentation Index > Fetch the complete documentation index at: https://fireworks.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Deployments > Configure and manage on-demand deployments on dedicated GPUs **New to deployments?** Start with our [Deployments Quickstart](/docs/getting-started/ondemand-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](https://fireworks.ai/pricing). * **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](https://fireworks.ai/contact). ## Creating & querying deployments **Create a deployment:** ```bash theme={null} # This command returns your accounts//deployments/ - save it for querying firectl deployment create accounts/fireworks/models/ --wait ``` **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: ```bash theme={null} firectl deployment create accounts/fireworks/models/ --region GLOBAL # recommended default firectl deployment create accounts/fireworks/models/ --region US firectl deployment create accounts/fireworks/models/ --region EUROPE firectl deployment create accounts/fireworks/models/ --region APAC ``` ### Check current placement ```bash theme={null} firectl deployment get ``` 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: ```bash theme={null} # 1. Create replacement with correct region firectl deployment create accounts/fireworks/models/ \ --deployment-shape \ --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 ``` See [Regions](/docs/deployments/regions) for mega-regions and hardware availability. See [Deployment shapes](#deployment-shapes) below to optimize for speed, throughput, or cost. **Query your deployment:** After creating a deployment, query it using this format: ``` accounts//deployments/ ``` You can find your deployment name anytime with `firectl deployment list` and `firectl deployment get `. **Example:** ``` accounts/alice/deployments/12345678 ``` ### Code examples ```python theme={null} from fireworks import Fireworks client = Fireworks() response = client.chat.completions.create( model="accounts/fireworks/models/gpt-oss-120b#", messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}] ) print(response.choices[0].message.content) ``` ```python theme={null} 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//deployments/", messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}] ) print(response.choices[0].message.content) ``` ```javascript theme={null} 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//deployments/", messages: [ { role: "user", content: "Explain quantum computing in simple terms", }, ], }); console.log(response.choices[0].message.content); ``` ```bash theme={null} curl https://api.fireworks.ai/inference/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $FIREWORKS_API_KEY" \ -d '{ "model": "accounts//deployments/", "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](/docs/faq/deployment/performance/optimization#performance-factors). * **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:** ```bash theme={null} # List available shapes firectl deployment-shape-version list --base-model # Create with a shape (shorthand) firectl deployment create accounts/fireworks/models/deepseek-v3 --deployment-shape throughput # 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 ``` Need even better performance with tailored optimizations? [Contact our team](https://fireworks.ai/contact). ## Managing & configuring deployments ### Basic management ```bash theme={null} # List all deployments firectl deployment list # Check deployment status firectl deployment get # Delete a deployment firectl deployment delete ``` 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](/docs/deployments/autoscaling#scaling-from-zero-behavior) for implementation details. ### GPU hardware Choose GPU type with `--accelerator-type`: * `NVIDIA_A100_80GB` * `NVIDIA_H100_80GB` * `NVIDIA_H200_141GB` GPU availability varies by [region](/docs/deployments/regions). See [Hardware selection guide→](https://docs.fireworks.ai/faq/deployment/ondemand/hardware-options#hardware-selection) ### Autoscaling Control replica counts, scale timing, and load targets for your deployment. See the [Autoscaling guide](/docs/deployments/autoscaling) for configuration options. ### Multiple GPUs per replica Use multiple GPUs to improve latency and throughput: ```bash theme={null} firectl deployment create --accelerator-count 2 ``` More GPUs = faster generation. Note that scaling is sub-linear (2x GPUs ≠ 2x performance). ## Preemptible deployments (eval / batch) 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 ```bash theme={null} firectl deployment create accounts/fireworks/models/ \ -a \ --deployment-id --display-name \ --deployment-shape accounts/fireworks/deploymentShapes/ \ --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: ```bash theme={null} firectl deployment get -a ``` When the eval is done, delete the deployment to release the borrowed capacity: ```bash theme={null} firectl deployment delete -a ``` ## Advanced * **[Speculative decoding](/docs/deployments/speculative-decoding)** - Speed up text generation using draft models or n-gram speculation * **[Quantization](/docs/models/quantization)** - Reduce model precision (e.g., FP16 to FP8) to improve speeds and reduce costs by 30-50% * **[Performance benchmarking](/docs/deployments/benchmarking)** - Measure and optimize your deployment's performance with load testing * **[Managing default deployments](/docs/deployments/managing-default-deployments)** - Control which deployment handles queries when using just the model name * **[Publishing deployments](/docs/deployments/publishing-deployments)** - Make your deployment accessible to other Fireworks users ## Next steps Configure autoscaling for optimal cost and performance Deploy your own models from Hugging Face Reduce costs with model quantization Choose deployment regions for optimal latency Purchase reserved GPUs for guaranteed capacity Fine-tune models for your specific use case