Cerebras Inference Connector for AI agents.

Q: How do I check which models are available for inference?

Use the listmodels tool. It will return a list of all supported models, including high-performance options like Llama 3.1, which you can then use in createchatcompletion.

Q: Can I process thousands of requests at once?

Yes. Use uploadfile to provide your JSONL data and then createbatch to start an asynchronous processing job. You can monitor progress with getbatch.

15 live capabilities

Get high-speed LLM inference and low-latency chat completions.

Live agent request Cerebras Inference / Connector

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AI Agent

Why people use Cerebras Inference

Cerebras Inference: Breaking the Latency Wall in AI Apps

With this Connector, you skip the waiting. By hooking your agent into the Wafer-Scale Engine, you get responses that actually keep up with human conversation. You get a snappy, usable product instead of a lagging demo.

What Vinkius changes

You get production-grade inference speeds without the typical cloud lag.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

Real-world use case 01

Solving the 'Slow Chat' Problem

A developer is tired of their chatbot taking 10 seconds to reply.
Real-world use case 02

The Data Crunching Wall

A data scientist needs to categorize 100,000 product reviews.
Real-world use case 03

Model Shopping Phase

A team needs to know which model handles 70b parameters best for their specific needs.

Complete set · 15capabilities

The complete Cerebras Inference capability set.

These are the exact actions your AI can choose when you ask it to work with Cerebras Inference.

Capability set01 / 04

01—04

4 capabilities in this set.

Part of 15 available through Cerebras Inference.

01 Capability
Cancel batch
Stops a running batch job immediately. Use this to kill unnecessary processes and save resources.
02 Capability
Create chat completion
Generates a conversational response using a structured message format. It's perfect for live chat apps.
03 Capability
Create completion
Generates text continuations from a single prompt string. Use this for simple text generation tasks.
04 Capability
Create batch
Starts a new batch job for asynchronous data processing. Use this for large scale inference.

Capability set02 / 04

05—08

4 capabilities in this set.

Part of 15 available through Cerebras Inference.

05 Capability
Delete file
Removes a file from your uploaded list. Keep your workspace clean by deleting old JSONL files.
06 Capability
Get batch
Checks the current status of a specific batch job. Use this to see if your data is finished processing.
07 Capability
Get file content
Downloads the raw content of an uploaded file. This lets you verify what the agent is about to process.
08 Capability
Get file
Retrieves the metadata for a specific file. Use this to check file names and IDs in your list.

Capability set03 / 04

09—12

4 capabilities in this set.

Part of 15 available through Cerebras Inference.

09 Capability
Get metrics
Pulls Prometheus-formatted operational metrics for your usage. Keep a close eye on your performance stats.
10 Capability
Get model
Fetches specific details for a single model. Use this to check parameters before running a job.
11 Capability
List batches
Lists all your current and past batch jobs. This helps you track your historical batch history.
12 Capability
List files
Shows all files you've uploaded for batching. Quickly see what's waiting in your queue.

Capability set04 / 04

13—15

3 capabilities in this set.

Part of 15 available through Cerebras Inference.

13 Capability
List models
Shows every model currently available on the platform. Use this to see your full options.
14 Capability
List public models
Retrieves model details without requiring an API key. Good for quick browsing of available options.
15 Capability
Upload file
Sends a JSONL file to the platform for batch processing. This is the first step for large data tasks.

Set up in minutes

One URL. Then ask Cerebras Inference to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Cerebras Inference from the conversation.

Choose your client

Live preview

Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_xAZFRBg8VLAlPUucEoLSiFDXRg3Jdk9xiofskaFP/mcp

Step 01

Open Connectors

In Claude Web or Claude Desktop, open Settings and choose Connectors.
Step 02

Add the URL

Choose Add custom connector, name it Cerebras Inference, and paste the URL above.
Step 03

Turn it on in chat

Select +, open Connectors, and enable Cerebras Inference for the conversation.

ChatGPT · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_xAZFRBg8VLAlPUucEoLSiFDXRg3Jdk9xiofskaFP/mcp

Step 01

Open MCP settings

On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
Step 02

Add the URL

Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Cerebras Inference URL.
Step 03

Save and start

Save the connection and enable Cerebras Inference in your conversation. Desktop may ask you to restart once.

Cursor · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "cerebras-inference": {
      "url": "https://edge.vinkius.com/vk_preview_xAZFRBg8VLAlPUucEoLSiFDXRg3Jdk9xiofskaFP/mcp"
    }
  }
}

Step 01

Open MCP Settings

Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
Step 02

Add the server config

Paste the JSON configuration above into the mcp.json file that opens
Step 03

Save the file

Cursor will automatically detect the new Connector
Step 04

Start using Cerebras Inference

Open Agent mode in chat and ask: "Using Cerebras Inference, help me...". 15 tools available

VS Code Copilot · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "cerebras-inference": {
      "url": "https://edge.vinkius.com/vk_preview_xAZFRBg8VLAlPUucEoLSiFDXRg3Jdk9xiofskaFP/mcp"
    }
  }
}

Step 01

Create MCP config

Create a .vscode/mcp.json file in your project root
Step 02

Add the server config

Paste the JSON configuration above
Step 03

Enable Agent mode

Open GitHub Copilot Chat and switch to Agent mode using the dropdown
Step 04

Start using Cerebras Inference

Ask Copilot: "Using Cerebras Inference, help me...". 15 tools available

Windsurf · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "cerebras-inference": {
      "url": "https://edge.vinkius.com/vk_preview_xAZFRBg8VLAlPUucEoLSiFDXRg3Jdk9xiofskaFP/mcp"
    }
  }
}

Step 01

Open MCP Settings

Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
Step 02

Add the server

Paste the JSON configuration above into mcp_config.json
Step 03

Save and reload

Windsurf will detect the new server automatically
Step 04

Start using Cerebras Inference

Open Cascade and ask: "Using Cerebras Inference, help me...". 15 tools available

Cline · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "cerebras-inference": {
      "url": "https://edge.vinkius.com/vk_preview_xAZFRBg8VLAlPUucEoLSiFDXRg3Jdk9xiofskaFP/mcp"
    }
  }
}

Step 01

Open Cline MCP Settings

Click the Connectors icon in the Cline sidebar panel
Step 02

Add remote server

Click "Add Connector" and paste the configuration above
Step 03

Enable the server

Toggle the server switch to ON
Step 04

Start using Cerebras Inference

Ask Cline: "Using Cerebras Inference, help me...". 15 tools available

Claude Code · Terminal command

Official guide ↗

Advanced setup

claude mcp add cerebras-inference --transport http "https://edge.vinkius.com/vk_preview_xAZFRBg8VLAlPUucEoLSiFDXRg3Jdk9xiofskaFP/mcp"

Step 01

Install Claude Code

Run npm install -g @anthropic-ai/claude-code if not already installed
Step 02

Add the Connector

Run the command above in your terminal
Step 03

Verify the connection

Run claude mcp to list connected servers, or type /mcp inside a session
Step 04

Start using Cerebras Inference

Ask Claude: "Using Cerebras Inference, show me...". 15 tools are ready

Where the request belongs

Work Cerebras Inference can move forward.

Built around the request

This is for the AI developer tired of 'thinking' dots, the data scientist with a mountain of data to process, and the product team shipping a latency-sensitive app.

AI Developer

You're building a live chatbot and need the agent to reply in under a second to keep users engaged.

Data Scientist

You need to run inference on a dataset of 500,000 rows and want to do it in a batch without hitting rate limits.

Product Manager

You're overseeing a production launch where slow LLM responses are the biggest risk to user retention.

Build the capability set

Each Connector adds new actions and data without changing how you work.

Browse Connectors

01 10 capabilities

Groq

Run large language models at unprecedented speed with custom LPU hardware that delivers real-time AI inference at massive scale.

View Connector

02 3 capabilities

SambaNova (AI Inference)

High-speed AI inference for Llama 3, DeepSeek, and MiniMax models via SambaNova's ultra-fast SN40L chips.

View Connector

03 9 capabilities

NVIDIA AI

Access LLMs, embeddings, code generation, and reasoning via NVIDIA API Catalog.

View Connector

04 8 capabilities

NVIDIA API Catalog

Cloud Engine proxy running native foundational completions natively utilizing active Nemotron and Llama3 architectures.

View Connector

05 20 capabilities

Replicate

Automate machine learning workflows via Replicate. run models, manage predictions, and search for AI assets directly from any AI agent.

View Connector

06 4 capabilities

DeepInfra (Serverless LLM Inference)

Run top-tier LLMs, image generation, and embeddings via DeepInfra's serverless infrastructure directly from your AI agent.

View Connector

Bring your own AI

Change the model, client or framework. Keep Cerebras Inference connected.

Claude
ChatGPT
Gemini
Cursor
VS Code
Windsurf
ZCode
Cline
Zed
Continue
Kiro
Roo Code
Zencoder
Goose
Void
Augment Code
Amp
Qodo
Tabnine
Pieces
Sourcegraph Cody
JetBrains
Warp
Amazon Q
Antigravity
BoltAI
Raycast
Jan
LM Studio
AnythingLLM
Open WebUI
Msty
Cherry Studio
LibreChat
TypingMind
Chorus
5ire
n8n
LangChain
LlamaIndex
CrewAI
Vercel AI SDK

Before you connect

Questions about Cerebras Inference.

The practical details behind the request, access and result.

How fast is Cerebras Inference compared to other options?

Cerebras Inference is designed for industry-leading speeds. By using the Wafer-Scale Engine, it provides some of the fastest inference times available today, making it ideal for real-time applications.

Can I use Cerebras Inference for my own chatbot?

Yes, it's perfect for that. You can use it to power conversational responses in your own apps, ensuring your users get replies without the usual cloud delays.

How do I run large data jobs with Cerebras Inference?

You can upload your JSONL files and start an asynchronous batch job. This allows you to process massive amounts of data in the background while you stay productive.

What models are supported on Cerebras Inference?

It supports several high-performance models, including the Llama 3.1 family. You can browse the full list of available models directly through your AI client.

Can I monitor my usage and performance?

Yes, you can pull Prometheus-formatted metrics. This helps you keep track of your operational stats and ensure everything is running efficiently.

Is it easy to set up with Claude or Cursor?

Yes, it's very straightforward. Once you've subscribed and added your API key, your AI client can start using the capabilities immediately.

How do I check which models are available for inference?

Use the list_models capability. It will return a list of all supported models, including high-performance options like Llama 3.1, which you can then use in create_chat_completion.

Can I process thousands of requests at once?

Yes. Use upload_file to provide your JSONL data and then create_batch to start an asynchronous processing job. You can monitor progress with get_batch.

Does this server support capability calling and structured outputs?

Yes. The create_chat_completion capability supports capabilities, tool_choice, and response_format parameters, allowing the model to interact with other functions or return valid JSON.

One connection away

Give your agent a direct line to Cerebras Inference.

Connect Cerebras Inference once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available

Cerebras Inference: Breaking the Latency Wall in AI Apps

You get production-grade inference speeds without the typical cloud lag.

Solving the 'Slow Chat' Problem

The Data Crunching Wall

Model Shopping Phase

Cancel batch

Create chat completion

Create completion

Create batch

Delete file

Get batch

Get file content

Get file

Get metrics

Get model

List batches

List files

List models

List public models

Upload file

Open Connectors

Add the URL

Turn it on in chat

Open MCP settings

Add the URL

Save and start

Open MCP Settings

Add the server config

Save the file

Start using Cerebras Inference

Create MCP config

Add the server config

Enable Agent mode

Start using Cerebras Inference

Open MCP Settings

Add the server

Save and reload

Start using Cerebras Inference

Open Cline MCP Settings

Add remote server

Enable the server

Start using Cerebras Inference

Install Claude Code

Add the Connector

Verify the connection

Start using Cerebras Inference

AI Developer

Data Scientist

Product Manager

Groq

SambaNova (AI Inference)

NVIDIA AI

NVIDIA API Catalog

Replicate

DeepInfra (Serverless LLM Inference)

Give your agent a direct line to Cerebras Inference.