> ## Documentation Index
> Fetch the complete documentation index at: https://inference-docs.cerebras.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Dedicated Inference

> Run production inference on capacity reserved for your organization.

Dedicated Inference reserves capacity for your organization.

Send inference requests to a stable **endpoint**. The endpoint routes each request to a **deployment**.

Use Dedicated Inference when you need predictable performance in production. Examples include real-time applications, customer-facing products, and high-volume pipelines. See [supported models](#supported-models).

## Key Benefits

<AccordionGroup>
  <Accordion title="Dedicated capacity">
    Your deployments run on capacity reserved for your organization.
  </Accordion>

  <Accordion title="Predictable performance">
    Reserved capacity provides predictable latency and throughput under load.
  </Accordion>

  <Accordion title="Custom weights">
    Deploy fine-tuned weights alongside standard model versions.
  </Accordion>

  <Accordion title="Configurable performance">
    Tune capacity, draft models, model configuration, and quantization for your workload.
  </Accordion>

  <Accordion title="Advanced features">
    Use every Shared Inference capability, plus fine-tuning, weight management, and service tier controls.
  </Accordion>
</AccordionGroup>

## Supported Models

Dedicated Inference supports many model families, parameter sizes, and variants, including `-instruct` and `-thinking`. You can also deploy custom weights and tune each deployment for your performance goals.

<AccordionGroup>
  <Accordion title="Alibaba Qwen: Qwen 3.8, Qwen3, and Qwen3-Coder" icon="https://mintcdn.com/cerebras-inference/k52j3v9j3Q8jlYv7/images/icons/qwen.svg?fit=max&auto=format&n=k52j3v9j3Q8jlYv7&q=85&s=5f17b5e6222779e638a8c283f8e511a8" width="16" height="16" data-path="images/icons/qwen.svg">
    **Qwen 3.8 27B**

    * [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)

    **Qwen3-235B-A22B**

    * [Qwen/Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507)
    * [Qwen/Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507)

    **Qwen3-32B**

    * [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B)

    **Qwen3-30B-A3B**

    * [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507)
    * [Qwen/Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507)

    **Small and tiny variants**

    * [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B)
    * [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
    * [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
    * [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)

    **Qwen3-Coder**

    * [Qwen/Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct)
    * [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct)
  </Accordion>

  <Accordion title="OpenAI: GPT-OSS" icon="openai">
    * [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b)
    * [openai/gpt-oss-safeguard-120b](https://huggingface.co/openai/gpt-oss-safeguard-120b)
    * [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
  </Accordion>

  <Accordion title="MiniMax: M2.X" icon="https://mintcdn.com/cerebras-inference/k52j3v9j3Q8jlYv7/images/icons/minimax.svg?fit=max&auto=format&n=k52j3v9j3Q8jlYv7&q=85&s=bb972b9e03b7f218e5d2776a141c29cb" width="24" height="24" data-path="images/icons/minimax.svg">
    * [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5)
    * [MiniMaxAI/MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1)
  </Accordion>

  <Accordion title="Google: Gemma 4" icon="google">
    * [google/gemma-4-31b-it](https://huggingface.co/google/gemma-4-31b-it)
  </Accordion>

  <Accordion title="Meta: Llama 3 and Llama 4" icon="meta">
    * [meta-llama/Llama-4-Maverick-17B-128E-Instruct](https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct) (402B total)
    * [meta-llama/Llama-4-Scout-17B-16E-Instruct](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct) (109B total)
    * [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)
  </Accordion>

  <Accordion title="Mistral: Mistral Small, Mistral Large 3, Devstral 2, and Mixtral" icon="https://mintcdn.com/cerebras-inference/k52j3v9j3Q8jlYv7/images/icons/mistralai.svg?fit=max&auto=format&n=k52j3v9j3Q8jlYv7&q=85&s=5e1e79f16460060e9825edacec580636" width="24" height="24" data-path="images/icons/mistralai.svg">
    * [mistralai/Mistral-Large-3-675B-Instruct-2512](https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512)
    * [mistralai/Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501)
    * [mistralai/Devstral-Small-2-24B-Instruct-2512](https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512)
    * [mistralai/Mathstral-7B-v0.1](https://huggingface.co/mistralai/Mathstral-7B-v0.1)
    * [mistralai/Codestral-22B-v0.1](https://huggingface.co/mistralai/Codestral-22B-v0.1)
  </Accordion>

  <Accordion title="Z.AI: GLM 4.X and GLM 5.X" icon="https://mintcdn.com/cerebras-inference/k52j3v9j3Q8jlYv7/images/icons/zai.svg?fit=max&auto=format&n=k52j3v9j3Q8jlYv7&q=85&s=06535f5280d8d19ba786406c66be6cf4" width="24" height="24" data-path="images/icons/zai.svg">
    * [zai-org/GLM-5.1](https://huggingface.co/zai-org/GLM-5.1)
    * [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5)
    * [zai-org/GLM-4.7](https://huggingface.co/zai-org/GLM-4.7)
    * [zai-org/GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash)
    * [zai-org/GLM-4.6](https://huggingface.co/zai-org/GLM-4.6)
    * [zai-org/GLM-4.5](https://huggingface.co/zai-org/GLM-4.5)
    * [zai-org/GLM-4.5-air](https://huggingface.co/zai-org/GLM-4.5-air)
  </Accordion>

  <Accordion title="Moonshot AI: Kimi K2.X" icon="https://mintcdn.com/cerebras-inference/k52j3v9j3Q8jlYv7/images/icons/moonshot.svg?fit=max&auto=format&n=k52j3v9j3Q8jlYv7&q=85&s=580edaa8ea7ace796f411c7244dddb25" width="24" height="24" data-path="images/icons/moonshot.svg">
    **Kimi K2.7 Code**

    * [moonshotai/Kimi-K2.7-Code](https://huggingface.co/moonshotai/Kimi-K2.7-Code)

    **Kimi K2.6**

    * [moonshotai/Kimi-K2.6](https://huggingface.co/moonshotai/Kimi-K2.6)
    * [moonshotai/Kimi-K2.5](https://huggingface.co/moonshotai/Kimi-K2.5)
    * [moonshotai/Kimi-K2-Instruct](https://huggingface.co/moonshotai/Kimi-K2-Instruct)
    * [moonshotai/Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking)
  </Accordion>

  <Accordion title="DeepSeek: V3.X" icon="https://mintcdn.com/cerebras-inference/k52j3v9j3Q8jlYv7/images/icons/deepseek.svg?fit=max&auto=format&n=k52j3v9j3Q8jlYv7&q=85&s=538f34c14c52d24010db84d0332cce36" width="16" height="16" data-path="images/icons/deepseek.svg">
    * [deepseek-ai/DeepSeek-V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2)
    * [deepseek-ai/DeepSeek-V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3.1)
    * [deepseek-ai/DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3)
  </Accordion>

  <Accordion title="StepFun: Step 3.X Flash" icon="https://mintcdn.com/cerebras-inference/5JGSuJfumLWIlNYj/images/icons/stepfun.svg?fit=max&auto=format&n=5JGSuJfumLWIlNYj&q=85&s=37cf81dd7ac6789eb7017fc053d0e816" width="84" height="84" data-path="images/icons/stepfun.svg">
    * [stepfun-ai/Step-3.7-Flash](https://huggingface.co/stepfun-ai/Step-3.7-Flash)
    * [stepfun-ai/Step-3.5-Flash](https://huggingface.co/stepfun-ai/Step-3.5-Flash)
  </Accordion>

  <Accordion title="ByteDance: Seed OSS" icon="https://mintcdn.com/cerebras-inference/k52j3v9j3Q8jlYv7/images/icons/bytedance.svg?fit=max&auto=format&n=k52j3v9j3Q8jlYv7&q=85&s=d90cbc6eb08537be002ff8bcfe3d0ca6" width="24" height="24" data-path="images/icons/bytedance.svg">
    * [ByteDance-Seed/Seed-OSS-36B-Instruct](https://huggingface.co/ByteDance-Seed/Seed-OSS-36B-Instruct)
  </Accordion>

  <Accordion title="ServiceNow: Apriel" icon="https://mintcdn.com/cerebras-inference/k52j3v9j3Q8jlYv7/images/icons/servicenow.svg?fit=max&auto=format&n=k52j3v9j3Q8jlYv7&q=85&s=9b7890d5d3b44e7363d4a70ca3b6b7b1" width="24" height="24" data-path="images/icons/servicenow.svg">
    * [ServiceNow-AI/Apriel-1.6-15b-Thinker](https://huggingface.co/ServiceNow-AI/Apriel-1.6-15b-Thinker)
  </Accordion>
</AccordionGroup>

<Note>
  Multimodal models, including Gemma 4, accept text and image inputs through Dedicated Inference. See [Image Inputs](/capabilities/image-inputs).
</Note>

## Features

Dedicated Inference includes all Shared Inference capabilities, plus:

* **Fine-tuning**: Deploy custom model weights behind your endpoint.
* **[Management API](/dedicated/management-api)**: Manage model versions, deployments, capacity, and endpoints through the API.
* **[Batch API](/capabilities/batch)**: Run large asynchronous workloads on reserved capacity.
* **[Predicted Outputs](/dedicated/predicted-outputs)**: Reduce latency by supplying expected output content.
* **[Service tiers](/capabilities/service-tiers)**: Prioritize requests to meet your SLA requirements.
* **[Metrics](/capabilities/metrics)**: Monitor endpoint requests, tokens, latency, and health with Prometheus-compatible metrics.

## Resource Model

Use these terms consistently when working with Dedicated Inference:

| Term | Meaning |
| - | - |
| **Model architecture** | The model structure that an endpoint supports, such as `qwen-3.8-27b`. Custom weights must use the same architecture. |
| **Model version** | An immutable snapshot of model weights. A version can contain Cerebras-provided or custom weights. |
| **Deployment** | A running model version and its serving settings, including capacity and replica count. You can replace a deployment without changing the endpoint ID. |
| **Endpoint** | A stable inference target for your applications, identified by an endpoint ID such as `my-org-qwen-3.8-27b`. An endpoint routes requests to one or more deployments. |
| **Replica** | One running copy of a deployment. Replicas provide capacity and availability. |
| **API route** | The HTTP path that receives a request, such as `/v1/chat/completions`. An API route is not an endpoint resource. |

An inference request uses these resources in order: **API route → endpoint → deployment → replica**.

<Note>
  For compatibility with OpenAI clients, the inference API uses the `model` field to select an inference target. For Shared Inference, use a model ID. For Dedicated Inference, use an endpoint ID.
</Note>

## Get Started

Dedicated Inference is available to enterprise customers. [Contact us](https://www.cerebras.ai/contact) to discuss your workload.