MiniMax M2.5

Large MoE model with native reasoning and tool calling. Uses the MiniMax-specific append-think reasoning format.

Setup

Install the Baseten CLI and sign in, then install the OpenAI SDK.

Install and sign in to Baseten

macOS or Linux
Windows

Terminal

brew tap basetenlabs/baseten
brew install baseten

Download and extract the binary, then move baseten.exe to a directory on your PATH:

Terminal

Invoke-WebRequest `
  https://github.com/basetenlabs/baseten-cli/releases/download/v0.4.0/baseten_0.4.0_windows_amd64.zip `
  -OutFile baseten.zip; Expand-Archive -Force baseten.zip .

For other platforms or a specific version, see the Baseten CLI install reference.

baseten auth login

Install the OpenAI SDK

uv pip install openai

Prefer not to install? Sign in with uvx truss login --browser and deploy with uvx truss push. This preset serves MiniMax M2.5 on H100:4 with expert-parallel sharding and Runai Streamer weight loading, optimized for maximum batch throughput.

Hardware

H100 × 4

Engine

vLLM (0.22.0-cu129 build)

Context

200K

Concurrency

Write the config

Create and move into the project directory:

mkdir minimax-m2.5-throughput && cd minimax-m2.5-throughput

Then create a file named config.yaml and paste the following:

config.yaml

model_name: "model:minimax-m2.5 preset:throughput"
model_metadata:
  description: >-
    MiniMax-M2.5 Mixture-of-Experts (Run:AI streamer loading), throughput on H100 × 4 with MiniMax parsers.
  repo_id: MiniMaxAI/MiniMax-M2.5
  example_model_input:
    messages:
      - role: system
        content: "You are a helpful assistant."
      - role: user
        content: "What is the meaning of life?"
    stream: true
    model: MiniMaxAI/MiniMax-M2.5
    max_tokens: 32768
    temperature: 0.7
  tags:
    - openai-compatible
base_image:
  image: vllm/vllm-openai:v0.22.0-cu129
weights:
  - source: "hf://MiniMaxAI/MiniMax-M2.5@main"
    mount_location: "/app/checkpoint/model"
    auth_secret_name: "hf_access_token"
    ignore_patterns:
      - "*.md"
      - "*.txt"
secrets:
  hf_access_token: null
environment_variables:
  VLLM_LOGGING_LEVEL: WARNING
  VLLM_ENGINE_READY_TIMEOUT_S: "3600"
docker_server:
  start_command: >-
    sh -c 'GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && SAFETENSORS_FAST_GPU=1 vllm serve /app/checkpoint/model
    --host 0.0.0.0
    --port 8000
    --served-model-name MiniMaxAI/MiniMax-M2.5
    --tensor-parallel-size $GPU_COUNT
    --enable-expert-parallel
    --trust-remote-code
    --load-format runai_streamer
    --disable-log-stats
    --max-num-seqs 64
    --max-num-batched-tokens 8192
    --tool-call-parser minimax_m2
    --reasoning-parser minimax_m2_append_think
    --enable-auto-tool-choice
    --enable-prefix-caching'
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/chat/completions
  server_port: 8000
resources:
  accelerator: H100:4
  use_gpu: true
runtime:
  predict_concurrency: 64
  health_checks:
    startup_threshold_seconds: 1800
    restart_threshold_seconds: 1200
    stop_traffic_threshold_seconds: 120

Flags

The start_command passes these flags to the engine. Each one controls a runtime or serving behavior:

Flag	Value	What it does
`--tensor-parallel-size`	`$GPU_COUNT`	Number of GPUs to shard the model across.
`--enable-expert-parallel`	(no value)	Shard MoE expert weights across tensor-parallel ranks instead of replicating them, reducing per-GPU memory for large MoE models.
`--trust-remote-code`	(no value)	Execute model-specific Python from the checkpoint (required for many Qwen, Phi, and custom architectures).
`--load-format`	`runai_streamer`	Weight loading backend. runai_streamer: Stream weights from object storage without materializing to disk.
`--disable-log-stats`	(no value)	Suppress periodic engine stats logging.
`--max-num-seqs`	`64`	Maximum number of concurrent sequences in the batch.
`--max-num-batched-tokens`	`8192`	Maximum total tokens processed per scheduler step.
`--tool-call-parser`	`minimax_m2`	Server-side parser that emits structured `tool_calls` on the response. minimax_m2: MiniMax M2 tool format.
`--reasoning-parser`	`minimax_m2_append_think`	Server-side parser that separates reasoning output into `reasoning_content`. minimax_m2_append_think: MiniMax M2 append-think format.
`--enable-auto-tool-choice`	(no value)	Let the model choose when to call tools without requiring `tool_choice: "required"`.
`--enable-prefix-caching`	(no value)	Reuse KV cache across requests that share a prefix.

Deploy

Push the config to Baseten with the Baseten CLI, or with the Truss CLI if you prefer it:

baseten model push

uvx truss push

You should see output similar to:

Pushing model "minimax-m2.5-throughput"...
Uploading model...
Uploaded model in 0s
✨ Model minimax-m2.5-throughput was successfully pushed ✨

  Model:       minimax-m2.5-throughput (abc1d2ef)
  Deployment:  xyz123
  Environment: production

🪵 View logs:
   deployment:   baseten model deployment logs --model-id abc1d2ef --deployment-id xyz123
   environment:  baseten model environment logs --model-id abc1d2ef --environment production  (once deployed)
   app:          https://app.baseten.co/models/abc1d2ef/logs/xyz123

🚀 Invoke your model:
   URL:  https://model-abc1d2ef.api.baseten.co/deployment/xyz123/predict
   CLI:  baseten model predict --model-id abc1d2ef

baseten model push prints your model ID (abc1d2ef in the example). The examples below use it wherever you see {model_id}, and read your API key from the BASETEN_API_KEY environment variable.

Call the model

Your deployment serves an OpenAI-compatible API. Now call your deployment to run inference:

Python
cURL

main.py

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["BASETEN_API_KEY"],
    base_url="https://model-{model_id}.api.baseten.co/environments/production/sync/v1",
)

response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M2.5",
    messages=[
        {"role": "user", "content": "What is machine learning?"}
    ],
)

print(response.choices[0].message.content)

curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "MiniMaxAI/MiniMax-M2.5",
    "messages": [
      {"role": "user", "content": "What is machine learning?"}
    ]
  }'

The server parses the model’s chain of thought into a separate reasoning_content field on the response. Read it alongside the final answer:

response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M2.5",
    messages=[
        {"role": "user", "content": "How many r's in strawberry?"}
    ],
)
print(response.choices[0].message.reasoning_content)  # chain of thought
print(response.choices[0].message.content)            # final answer

To let the model call tools, pass a tools array. The server returns structured tool_calls on the response:

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"],
        },
    },
}]

response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M2.5",
    messages=[
        {"role": "user", "content": "What's the weather in Paris?"}
    ],
    tools=tools,
)
print(response.choices[0].message.tool_calls)

Examples

Models

Engines

Custom Docker servers

Custom Python models

Chains

Setup

Hardware

Engine

Context

Concurrency

Write the config

Flags

Deploy

Call the model

Next steps

Call your model

Autoscaling

​Setup

Hardware

Engine

Context

Concurrency

​Write the config

​Flags

​Deploy

​Call the model

​Next steps

Call your model

Autoscaling

Setup

Write the config

Flags

Deploy

Call the model

Next steps