How to deploy large models

Reference, synced 2026-06-13.

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you can deploy it to obtain an independent inference service with dedicated resources. This meets your business needs for varying performance requirements, such as high concurrency and low latency.

Billing Methods

Before deployment, you can check the estimated hourly cost for different models in the Model Deployment Console.

Note

You cannot change the billing method after creating the service. To switch, you must unpublish the deployed model and redeploy it.

	Provisioned Throughput Unit (PTU) (High throughput; High performance)	Model Unit (Custom performance metrics; Resource isolation)	Token Usage (Pay-as-you-go after fine-tuning / Effect validation)


	Provisioned Throughput Unit (PTU) (High throughput; High performance)	Model Unit (Custom performance metrics; Resource isolation)	Token Usage (Pay-as-you-go after fine-tuning / Effect validation)
Definition	A deployment method that reserves platform resources to guarantee a specific TPM throughput capacity. No rate limiting applies within the guaranteed quota.	A deployment method that allocates dedicated computing power based on usage duration and the number of model units.	A deployment method that bills based on input and output tokens generated per API call.
Advantages	1. Provides stable throughput capacity, lower latency, and stronger resource guarantees for high-load production environments. 2. Typically improves TPS (tokens per second) by about 1.5–2.0× compared to token-based billing. 3. Supports auto-renewal.	1. Performance metrics like latency and throughput are customizable. 2. Supports auto-renewal.	No charge when not in use.
Supported Models	Some preset models	Some preset models and all fine-tuned models	Some LoRA-fine-tuned models
Use Cases	1. Intelligent customer service in banking apps (stable traffic, requires guaranteed concurrent experience). 2. Real-time content moderation on social platforms (needs stable processing of predictable pipeline tasks). 3. Public cloud translation APIs (provides baseline service guarantees for standard package users).	1. E-commerce exclusive fine-tuned LLMs (deploy private models, manually scale out during sales promotions). 2. Molecular screening models for pharmaceutical companies (requires dedicated resources for long-running tasks). 3. Autonomous driving simulation (requires continuous long-duration computation).	Effect validation for fine-tuned models
Billing Illustration
Billing Method	Based on usage duration and provisioned throughput Pay-as-you-go, daily billing	Based on usage duration and number of model units Pay-as-you-go, monthly billing	Based on model token usage Pay-as-you-go
Scaling Method	Self-service adjustment of throughput	Self-service adjustment of model unit count	Submit a request in the console and wait for manual review.

Product Constraints	1. Prepaid billing is daily. No early refunds. 2. If usage exceeds purchased throughput within a time unit, it automatically switches to the model invocation service provided by Model Studio.	For prepaid purchases, if you cancel within the first month, the daily rate (≈ monthly rate / 30) is billed at 1.2 × the standard rate.	1. Only supports some efficiently fine-tuned (LoRA) models. 2. Automatically releases if unused for one month.

To view token usage and historical invocation statistics for individual calls, go to: Model Monitoring.

Billing Details

Usage-Based Billing (Provisioned Throughput)

Usage-Based Billing (Model Unit)

Token Usage-Based Billing

Cost = Usage Duration × (Input TPM Unit Price × Input TPM + Output TPM Unit Price × Output TPM)

Postpaid billing is hourly: usage duration is in hours, and unit prices come from the "1 Hour Continuous" column below. Prepaid billing is daily: usage duration is in days, and unit prices come from the "1 Day Continuous" column below.

Prepaid orders take effect immediately after payment and expire at 23:59 on day N. Orders placed after 22:00 extend the expiration date by one day.
After a prepaid order expires, the service stops after a 2-hour grace period. Resources are released 14 hours after stopping.
Prepaid orders cannot be terminated early.
For postpaid billing, if your account has overdue payment, deployed resources remain reserved and continue billing for 24 hours before automatic release.

If model input exceeds the maximum token limit or purchased TPM capacity, the call automatically switches to the pay-as-you-go mode for that model. Inference performance may degrade and will be subject to public traffic controls for the current snapshot model in your workspace. Costs follow the standard pay-as-you-go model invocation rates.

In this case, the API response header includes: x-dashscope-ptu-overflow:true.
For TPM statistics, go to: Model Monitoring.

For specific refund rules during scale-in (downgrade), see: Downgrade Refund Policy.

Singapore

China (Beijing)

Qwen

QwenVL

DeepSeek


Model Name	Model Code	Max Input Tokens	Postpaid Input Per 10K TPM/hour	Postpaid Output Per 1K TPM/hour	Prepaid Input Per 10K TPM/day	Prepaid Output Per 1K TPM/day
Qwen3.6-Plus-2026-04-02	qwen3.6-plus-2026-04-02	128,000	$1.2	$0.72	$14.4	$8.64

Qwen3.5-Plus-2026-04-20	qwen3.5-plus-2026-04-20	128,000	$0.96	$0.576	$11.52	$6.912


Model Name	Model Code	Max Input Tokens	Postpaid Input Per 10K TPM/hour	Postpaid Output Per 1K TPM/hour	Prepaid Input Per 10K TPM/day	Prepaid Output Per 1K TPM/day
Qwen3-VL-Plus-2025-09-23	qwen3-vl-plus-2025-09-23	128,000	$0.48	$0.384	$5.76	$4.608


Model Name	Model Code	Max Input Tokens	Postpaid Input Per 10K TPM/hour	Postpaid Output Per 1K TPM/hour	Prepaid Input Per 10K TPM/day	Prepaid Output Per 1K TPM/day
DeepSeek-v3.2	deepseek-v3.2	64,000	$2.05	$0.616	$24.62	$7.387

Qwen

DeepSeek

QwenVL

More Models


Model Name	Model Code	Max Input Tokens	Postpaid Input Per 10K TPM/hour	Postpaid Output Per 1K TPM/hour	Prepaid Input Per 10K TPM/day	Prepaid Output Per 1K TPM/day
Qwen3.7-Max-2026-05-20	qwen3.7-max-2026-05-20	128,000	$3.96	$1.188	$47.53	$14.258

Qwen3.6-Flash-2026-04-16	qwen3.6-flash-2026-04-16	128,000	$0.4	$0.238	$4.75	$2.852
Qwen3.6-Plus-2026-04-02	qwen3.6-plus-2026-04-02	128,000	$0.67	$0.397	$7.93	$4.753

Qwen3.5-Plus-2026-04-20	qwen3.5-plus-2026-04-20	128,000	$0.26	$0.16	$3.17	$1.9

Qwen3-Max-2025-09-23	qwen3-max-2025-09-23	128,000	$1.11	$0.45	$13.32	$5.4

Qwen-Flash-2025-07-28	qwen-flash-2025-07-28	128,000	$0.06	$0.06	$0.72	$0.72
Qwen-Plus-2025-12-01	qwen-plus-2025-12-01	128,000	$0.28	Non-thinking: $0.07 Thinking: $0.28	$3.36	Non-thinking: $0.84 Thinking: $3.36


Model Name	Model Code	Max Input Tokens	Postpaid Input Per 10K TPM/hour	Postpaid Output Per 1K TPM/hour	Prepaid Input Per 10K TPM/day	Prepaid Output Per 1K TPM/day
DeepSeek-v4-Pro	deepseek-v4-pro	64,000	$5.94	$1.188	$71.3	$14.26
DeepSeek-v3.2	deepseek-v3.2	64,000	$1.04	$0.16	$12.48	$1.92
DeepSeek-v3	deepseek-v3	64,000	$0.99	$0.396	$11.9	$4.75


Model Name	Model Code	Max Input Tokens	Postpaid Input Per 10K TPM/hour	Postpaid Output Per 1K TPM/hour	Prepaid Input Per 10K TPM/day	Prepaid Output Per 1K TPM/day
Qwen3-VL-Plus-2025-09-23	qwen3-vl-plus-2025-09-23	128,000	$0.35	$0.35	$4.2	$4.2


Model Name	Model Code	Max Input Tokens	Postpaid Input Per 10K TPM/hour	Postpaid Output Per 1K TPM/hour	Prepaid Input Per 10K TPM/day	Prepaid Output Per 1K TPM/day
GLM-5.1	glm-5.1	64,000	$2.97	$1.19	$35.65	$14.26

Cost = Usage Duration (hours) × Number of Model Units × Unit Price per Model Unit

For postpaid scenarios, use the "Hourly Rate" column below for the unit price. For prepaid monthly billing, the formula becomes Number of Months × Number of Model Units × Monthly Unit Price.

For prepaid purchases canceled within the first month, the daily rate (≈ monthly rate / 30) is billed at 1.2 × the standard rate (partial days count as full days).

Note

Compute resources for postpaid model units are allocated on a first-come, first-served basis. If purchase fails, you receive a full refund.

Singapore

China (Beijing)

Text Generation

Multimodal


Model Name	Model Code	Model Unit Spec	Hourly Rate ($) Minimum billing: minute	Monthly Rate ($) Minimum billing: day
Qwen3.6-Plus-2026-04-02	qwen3.6-plus-2026-04-02	MU1 x 8	$88	$41,832

Qwen3.5-39B-A17B	qwen3.5-397b-a17b	MU2 x 8	$112	$52,392
Qwen3.5-35B-A3B	qwen3.5-35b-a3b	MU2 x 8	$112	$52,392

Qwen3-32B	qwen3-32b	MU1 x 4	$44	$20,916
MU2 x 8	$112	$52,392
Qwen3-14B	qwen3-14b	MU1 x 4	$44	$20,916

GLM-5.1	glm-5.1	MU2 x 8	$112	$52,392

DeepSeek-V4-Flash	deepseek-v4-flash	MU1 x 8	$88	$41,832


Model Name	Model Code	Model Unit Spec	Hourly Rate ($) Minimum billing: minute	Monthly Rate ($) Minimum billing: day
Qwen3-VL-32B-Instruct	qwen3-vl-32b-instruct	MU2 x 8	$112	$52,392
Qwen3-VL-8B-Instruct	qwen3-vl-8b-instruct	MU1 x 2	$22	$10,458

Model types:

Instruct - The model performs inference in non-thinking mode after deployment.

Text Generation

Multimodal

Qwen

GLM

DeepSeek

Other Models


Model Name	Model Code	Model Unit Spec	Hourly Rate ($) Minimum billing: minute	Monthly Rate ($) Minimum billing: day
Qwen3.7-Plus-2026-05-26	qwen3.7-plus-2026-05-26	MU3 x 8	$150.72	$72,577.152

Qwen3.6-35B-A3B	qwen3.6-35b-a3b	MU8 x 1	$6.464	$3,080.477
MU9 x 1	$7.014	$3,383.024
Qwen3.6-27B	qwen3.6-27b	MU9 x 1	$7.014	$3,383.024
Qwen3.6-Flash-2026-04-16	qwen3.6-flash-2026-04-16	MU1 x 2	$14.852	$7,183.564
Qwen3.6-Plus-2026-04-02	qwen3.6-plus-2026-04-02	MU1 x 8	$59.408	$28,734.256

Qwen3.5-397B-A17B	qwen3.5-397b-a17b	MU2 x 8	$69.312	$33,044.72
MU3 x 8	$150.72	$72,577.152
MU6 x 16	$55.008	$26,599.92
Qwen3.5-122B-A10B	qwen3.5-122b-a10b	MU1 x 4	$29.704	$14,367.128
MU2 x 8	$69.312	$33,044.72
MU6 x 16	$55.008	$26,599.92
MU9 x 2	$14.028	$6,766.048
Qwen3.5-35B-A3B	qwen3.5-35b-a3b	MU1 x 2	$14.852	$7,183.564
MU2 x 8	$69.312	$33,044.72
MU8 x 1	$6.464	$3,080.477
MU9 x 1	$7.014	$3,383.024
Qwen3.5-27B	qwen3.5-27b	MU9 x 1	$7.014	$3,383.024
Qwen3.5-9B	qwen3.5-9b	MU8 x 1	$6.464	$3,080.477
MU9 x 1	$7.014	$3,383.024
Qwen3.5-Flash-2026-02-23	qwen3.5-flash-2026-02-23	MU1 x 2	$14.852	$7,183.564
Qwen3.5-Plus-2026-02-15	qwen3.5-plus-2026-02-15	MU1 x 8	$59.408	$28,734.256
MU3 x 8	$150.72	$72,577.152

Qwen3-235B-A22B-Instruct	qwen3-235b-a22b-instruct-2507	MU1 x 4	$29.704	$14,367.128
MU2 x 8	$69.312	$33,044.72
Qwen3-Next-80B-A3B-Instruct	qwen3-next-80b-a3b-instruct	MU1 x 2	$14.852	$7,183.564
Qwen3-32B	qwen3-32b	MU1 x 4	$29.704	$14,367.128
MU6 x 4	$13.752	$6,649.98
Qwen3-30B-A3B	qwen3-30b-a3b	MU9 x 2	$14.028	$6,766.048
Qwen3-30B-A3B-Instruct-2507	qwen3-30b-a3b-instruct-2507	MU1 x 4	$29.704	$14,367.128
MU2 x 8	$69.312	$33,044.72
Qwen3-8B	qwen3-8b	MU1 x 2	$14.852	$7,183.564
MU2 x 2	$17.328	$8,261.18
MU5 x 1	$2.888	$1,394.329
Qwen3-4B	qwen3-4b	MU1 x 2	$14.852	$7,183.564
MU5 x 1	$2.888	$1,394.329
Qwen3-1.7B	qwen3-1.7b	MU1 x 2	$14.852	$7,183.564
MU5 x 1	$2.888	$1,394.329
Qwen3-Max-2025-09-23	qwen3-max-2025-09-23	MU2 x 8	$69.312	$33,044.72
MU3 x 8	$150.72	$72,577.152

Qwen2.5-72B	qwen2.5-72b-instruct	MU1 x 4	$29.704	$14,367.128
Qwen2.5-32B	qwen2.5-32b-instruct	MU1 x 4	$29.704	$14,367.128
Qwen2.5-14B	qwen2.5-14b-instruct	MU1 x 2	$14.852	$7,183.564
Qwen2.5-7B	qwen2.5-7b-instruct	MU1 x 2	$14.852	$7,183.564
MU5 x 1	$2.888	$1,394.329
Qwen2.5-3B-Instruct	qwen2.5-3b-instruct	MU5 x 1	$2.888	$1,394.329

Qwen-Flash-2025-07-28	qwen-flash-2025-07-28	MU1 x 4	$29.704	$14,367.128
Qwen-Plus-2025-07-28	qwen-plus-2025-07-28	MU1 x 4	$29.704	$14,367.128
Qwen-Plus-2025-12-01	qwen-plus-2025-12-01	MU1 x 4	$29.704	$14,367.128


Model Name	Model Code	Model Unit Spec	Hourly Rate ($) Minimum billing: minute	Monthly Rate ($) Minimum billing: day
GLM-5	glm-5	MU3 x 8	$150.72	$72,577.152
GLM-4.7	glm-4.7	MU6 x 16	$55.008	$26,599.92


Model Name	Model Code	Model Unit Spec	Hourly Rate ($) Minimum billing: minute	Monthly Rate ($) Minimum billing: day
DeepSeek-V4-Flash	deepseek-v4-flash	MU1 x 8	$59.408	$28,734.256
DeepSeek-V3.2	deepseek-v3.2	MU2 x 8	$69.312	$33,044.72


Model Name	Model Code	Model Unit Spec	Hourly Rate ($) Minimum billing: minute	Monthly Rate ($) Minimum billing: day
MiniMax-M2.5	MiniMax-M2.5	MU1 x 8	$59.408	$28,734.256

Kimi-K2.5	kimi-k2.5	MU2 x 8	$69.312	$33,044.72

QwenVL

Qwen Omni


Model Name	Model Code	Model Unit Spec	Hourly Rate ($) Minimum billing: minute	Monthly Rate ($) Minimum billing: day
Qwen3-VL-235B-A22B-Instruct	qwen3-vl-235b-a22b-instruct	MU1 x 4	$29.704	$14,367.128
Qwen3-VL-235B-A22B-Thinking	qwen3-vl-235b-a22b-thinking	MU1 x 4	$29.704	$14,367.128
Qwen3-VL-32B-Instruct	qwen3-vl-32b-instruct	MU2 x 8	$69.312	$33,044.72
Qwen3-VL-8B-Instruct	qwen3-vl-8b-instruct	MU1 x 2	$14.852	$7,183.564
Qwen3-VL-Flash-2025-10-15	qwen3-vl-flash-2025-10-15	MU1 x 4	$29.704	$14,367.128
Qwen3-VL-Plus-2025-09-23	qwen3-vl-plus-2025-09-23	MU1 x 4	$29.704	$14,367.128

Qwen-VL-Max-2025-08-13	qwen-vl-max-2025-08-13	MU6 x 4	$13.752	$6,649.98
Qwen-VL-OCR-2025-11-20	qwen-vl-ocr-2025-11-20	MU6 x 4	$13.752	$6,649.98


Model Name	Model Code	Model Unit Spec	Hourly Rate ($) Minimum billing: minute	Monthly Rate ($) Minimum billing: day
Qwen3.5-Omni-Flash	qwen3.5-omni-flash	MU8 x 1	$6.464	$3,080.477
MU9 x 1	$7.014	$3,383.024
Qwen3.5-Omni-Plus	qwen3.5-omni-plus	MU9 x 8	$56.112	$27,064.192

Model types:

Instruct - The model performs inference in non-thinking mode after deployment.
Thinking - The model performs inference in thinking mode after deployment.

Cost = Input Token Count × Input Unit Price + Output Token Count × Output Unit Price (Minimum billing unit: 1 token)

Token usage billing is available only after you complete SFT fine-tuning on the following base models to create a custom model.

Singapore


Base Model	Model Code	Input CNY/1K Tokens	Output CNY/1K Tokens
Qwen3-14B	qwen3-14b	$0.00035	Non-thinking mode: $0.0014 Thinking mode: $0.0042

To deploy more models, see this solution and choose the best deployment option for your business needs.

Deployment Methods

Deploy models in the console using these steps:

If you see a permission error, see: What to do if you lack permissions during deployment?


1. Go to the Model Deployment Console.
2. Enter a service name, select a model and billing method, keep other settings as default, and click Confirm.
3. When the deployment status shows Running, the model is successfully deployed. Important You incur charges after successful deployment.

Deployment Configuration

Model Unit


Configuration Item	Details
Service Name	Custom name for your deployment service.
Select Model	Choose a model to deploy, including preset platform models and fine-tuned models.
Model Unit Type	Select a deployment specification. Different specs provide different computing power and performance.
Initial Replicas	Set the initial number of deployment replicas. This affects the service's concurrent processing capacity.
Deployment Template	Select a deployment template (such as "Single-node Deployment"). Different templates offer different resource allocation schemes. Available only for model unit billing mode.
Configure Inference Mode	For some models deployed using Model Unit, you can configure inference mode, max context length, and more. - Instruct - The model performs inference in non-thinking mode after deployment. - Thinking - The model performs inference in thinking mode after deployment.
Max Context Length	Available for some models deployed using Model Unit. Max context length depends on the model type.
Service Rate Limiting	Available for some models deployed using Model Unit. You can limit RPM (requests per minute) and TPM (tokens per minute).

Deployment List Page

After successful deployment, view and manage all deployment services on the deployment list page. This page shows:

Service Name: Name of the deployment service. Click to view deployment details.
Model Name: Model used for deployment.
Model Code: Unique identifier generated after successful deployment. Use this code to specify the model in API calls.
Deployment Status / Event Status: Includes Pending, Deploying, Running, Deployment Failed, Unpublishing, Service Paused, Stopped, Deleting, Opt-out/Overdue Payment Stopped, Resuming, Running (Upgrading/Downgrading), Running (Upgrade/Downgrade Failed), etc.
Billing Method: Current billing method for the deployment service.
Deployment Details: Configuration information such as model unit type and replica count.
Rate Limiting Details: Shows RPM (requests per minute), TPM (tokens per minute), and other rate limiting settings.
Service Duration: Shows creation time and expiration time of the deployment service.
Operations: Based on deployment status and billing method, you can update, monitor, scale, renew, unpublish, delete, or test the service.

Calling After Deployment

After successful deployment, call the model using OpenAI compatible, DashScope, or the Assistant SDK.

When invoking a successfully deployed model, the value of model should be the model code generated after successful deployment. Go to the Model Deployment Console interface to obtain the Model Code.

DashScope

OpenAI-Compatible API

python

import os
import dashscope

messages = [\
    {"role": "system", "content": "You are a helpful assistant."},\
    {"role": "user", "content": "Who are you?"},\
]
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
response = dashscope.Generation.call(
    # If you haven't set an environment variable, replace the next line with: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen3-max-xxx-xxx",  # Replace with your deployed model code
    messages=messages,
    result_format="message",
    enable_thinking=False,
)
print(response)

python

import os
from openai import OpenAI

client = OpenAI(
    # If you haven't set an environment variable, replace the next line with: api_key="sk-xxx",
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="qwen3-max-xxx-xxx",  # Replace with your deployed model code
    messages=[\
        {"role": "system", "content": "You are a helpful assistant."},\
        {"role": "user", "content": "Who are you?"},\
    ],
    extra_body={"enable_thinking": False},
)
print(completion)

Scaling Deployment Services

Provisioned Throughput (time-based): Click the Scaling button to manually adjust instance count. For specific refund rules, see: Downgrade Refund Policy.
Model Unit (time-based): Click the Scaling button to manually adjust instance count.

You can also configure auto-scaling policies (including scaling thresholds, min/max replicas, scheduled scaling, etc.) using the scaling configuration button in the operations column.

Unpublishing Deployment Services

Go to the Model Deployment Console, find the service to stop, and click the corresponding action based on billing type:

Prepaid Model Unit: Click Deactivate and confirm.
Postpaid: Click Delete and confirm.

Billing stops after this operation.

Other Operations

Besides unpublishing, the operations column on the deployment list page supports:

Update: Update the model version of a deployed service. Supports full or phased updates (canary release).
Delete: Directly delete pay-as-you-go services to stop billing.
Renew: Extend service duration for prepaid services. Supports auto-renewal.
Purchase Capacity Package: Buy capacity packages for provisioned throughput deployments.

FAQ

Can I upload and deploy my own model?

Uploading and deploying custom models is currently unsupported. We recommend monitoring Alibaba Cloud Model Studio for future updates.

Additionally, Alibaba Cloud Platform for AI (PAI) supports deploying custom models. See PAI-LLM Large Language Model Deployment for deployment methods.

What should I do if I get a permission error during deployment?

If you see "Missing module permissions", ensure your account has Model Deployment - Operation permissions in the workspace's permission management page.

If you cannot proceed, contact your organization or IT administrator to add the required permissions or check the issue.

If you get the error " xx workspace lacks deployment permissions for model xx", go to the Model Studio Workspace Management page and add deployment permissions for the model to the corresponding workspace.

API error: Workspace xxx does not have deployment privilege for model xxxx.

If you lack permissions, contact your organization or IT administrator to add them or perform the action on your behalf.

Previous: Fine-tuning video generation modelsNext: Model Import

Is this page helpful?

Billing Methods

Billing Details

Deployment Methods

Deployment Configuration

Deployment List Page

Calling After Deployment

Scaling Deployment Services

Unpublishing Deployment Services

Other Operations

FAQ

Can I upload and deploy my own model?

What should I do if I get a permission error during deployment?

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How to deploy large models

Models

Models

GenAI Application

AI Service

AI Use Case

Overview

Why Alibaba Cloud

Customers and Insights

What's New

Products

Featured Products

Related Programs

Solutions

Pricing

Overview & Tools

Optimize your cost

Resources

Technical Resources

Training&Certification

Developer Hub

Partners

Marketplace

Find a Partner

Become a Partner

Support

Support & Professional Services

Contact us

Language

Locale

Billing Methods

Billing Details

Deployment Methods

Deployment Configuration

Deployment List Page

Calling After Deployment

Scaling Deployment Services

Unpublishing Deployment Services

Other Operations

FAQ

Can I upload and deploy my own model?

What should I do if I get a permission error during deployment?

Sales Support

Technical Support

Connect & Report Abuse