One endpoint,
200+ models

Keep your code, change the model ID. Chat, vision, and reasoning on H200 GPUs

curl https://api.gmi-serving.com/v1/chat/completions \
  -H "Authorization: Bearer $GMI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-V4-Pro",
    "stream": true,
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Models Available: 200+
Data Center Coverage: Global
Uptime target: 99.9%

Four ways in

Start with the piece you need. Each links to its reference

Inference API

Chat, vision, and reasoning on one OpenAI-compatible endpoint

API Reference

SDK

Python and TypeScript clients. Drop-in for the OpenAI SDK

SDK Docs

AgentBox

Deploy and run agents on managed compute

Open AgentBox

Docs

Quickstarts, references, and code samples

Read Docs

First request

Create a key, pick a model, read the stream

01
Create a key
Generate an API key in the console
02
Pick a model
Copy a model id, or list them with GET /v1/models
03
Send a request
Call the endpoint and read the stream
04
Scale
Switch to a dedicated endpoint for fixed latency

python · openai client

# pip install openai
from openai import OpenAI
client = OpenAI(base_url="https://api.gmi-serving.com/v1",
                api_key=GMI_API_KEY)  # from console.gmicloud.ai

stream = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Pro",  # any id from /v1/models
    stream=True,
    messages=[{"role":"user",
               "content":"Summarize this paragraph."}])

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")