GMI Cloud

The GMI Cloud provider offers access to open-weight language models through GMI Cloud's GPU inference platform, over an OpenAI-compatible API. Start with AI Gateway for models in its catalog (recommended), or use @ai-sdk/gmicloud to connect directly to GMI Cloud.

import { generateText } from 'ai';

const { text } = await generateText({
  model: 'anthropic/claude-sonnet-5.5',
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});

console.log(text);

Using AI Gateway

The AI SDK uses AI Gateway by default for model strings. Gateway provides access to models across providers through a single API key, with usage and cost tracking. Install ai:

pnpm add ai

npm install ai

yarn add ai

bun add ai

Set an AI Gateway API key in your environment:

export AI_GATEWAY_API_KEY=your_api_key_here

Vercel deployments can also use OIDC authentication. See the AI Gateway provider documentation for configuration and the model catalog for available models.

The example sets providerOptions.gateway.order to ['gmicloud']. This sends the request to GMI Cloud first, then falls back to other available providers of the same model if GMI Cloud is unavailable or the request fails. order sets a preference; it does not restrict fallback to GMI Cloud. See provider ordering.

The dropdown lists models served by GMI Cloud through Gateway with a corresponding direct-provider example. Provider availability depends on the model and your team's Provider Allowlist.

Using @ai-sdk/gmicloud

For direct access to GMI Cloud, install ai and @ai-sdk/gmicloud:

pnpm add ai @ai-sdk/gmicloud

npm install ai @ai-sdk/gmicloud

yarn add ai @ai-sdk/gmicloud

bun add ai @ai-sdk/gmicloud

Set your GMI Cloud API credential in the GMI_CLOUD_APIKEY environment variable:

export GMI_CLOUD_APIKEY=your_api_key_here

AI Gateway does not expose every API in @ai-sdk/gmicloud. Use the direct provider for provider-specific APIs or custom connection settings.

API keys can be obtained from the GMI Cloud console.

Provider Instance

You can import the default provider instance gmicloud from @ai-sdk/gmicloud:

import { gmicloud } from '@ai-sdk/gmicloud';

For custom configuration, you can import createGmicloud and create a provider instance with your settings:

import { createGmicloud } from '@ai-sdk/gmicloud';

const gmicloud = createGmicloud({
  apiKey: process.env.GMI_CLOUD_APIKEY ?? '',
});

You can use the following optional settings to customize the GMI Cloud provider instance:

• baseURL string

  Use a different URL prefix for API calls. The default prefix is https://api.gmi-serving.com/v1.

• apiKey string

  API key that is being sent using the Authorization header. It defaults to the GMI_CLOUD_APIKEY environment variable.

• headers Record<string,string>

  Custom headers to include in the requests.

• fetch FetchFunction

  Custom fetch implementation.

Language Models

import { gmicloud } from '@ai-sdk/gmicloud';
import { generateText } from 'ai';

const { text } = await generateText({
  model: gmicloud('deepseek-ai/DeepSeek-V4-Flash-0731'),
  prompt: 'What is the capital of France?',
});

GMI Cloud serves an evolving catalog of open-weight models, so model ids are typed as string. Embedding and image models are not supported.

Error diagnostics

GMI Cloud's edge reports a generic banner in error.message on rejections and nests the backend engine's diagnostic in error.details. This provider unwraps the nested diagnostic, so AI_APICallError.message carries the engine's reason (e.g. The request is invalid: Invalid max_tokens value, the valid range of max_tokens is [1, 393216].) instead of Backend request failed with status 400.