Alibaba Cloud ECS Launch Template Specifications

Alibaba Cloud ECS Launch Template Specifications | Generated by AI

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Question: How is the Alibaba Cloud ECS instance configuration shown in the launch template configured?

Answer:

Here is a full breakdown of your Alibaba Cloud ECS launch template configuration:

Instance Overview

Parameter	Value
Billing	Pay-As-You-Go (按量付费)
Region	China South 1 (Shenzhen)
Availability Zone	Auto-assigned
Instance Type	GPU Compute `ecs.gn7t-c16g1.4xlarge`
vCPU	16 vCPU
Memory	94 GiB
Instance Name	`launch-advisor-20260328`

GPU Specification

The instance belongs to the gn7t family. Based on the naming convention (gn7t = 7th-gen GPU compute, t typically denotes a specific GPU variant in this generation):

GPU Count: 1 card (indicated by g1 in the name)
CPU: High-performance Intel or AMD processor (7th-gen series)
vCPU / Memory: 16 vCPU / 94 GiB — a relatively generous memory-to-CPU ratio suitable for AI inference workloads

Note: The exact GPU model for gn7t is not clearly documented in publicly available specs. It is closely related to the gn7 family. If you need to confirm the exact GPU card (e.g., whether it’s NVIDIA A10, A30, or similar), check the Alibaba Cloud ECS console or the official GPU instance family page.

Network

Parameter	Value
Network Type	VPC (Virtual Private Cloud)
VPC	Default VPC
Subnet (Switch)	Default Switch
Public Bandwidth	Pay-by-traffic, up to 100 Mbps
Security Group	Default Security Group

Storage

Parameter	Value
System Disk	ESSD Cloud Disk, 40 GiB
Disk Performance Level	PL0 (up to 10,000 IOPS)
Release with Instance	Yes
File Backup	Activated

Notes on Storage:

PL0 is the entry-level ESSD tier with a max of 10,000 IOPS. For heavy I/O workloads (e.g., large model training), consider upgrading to PL1 (50,000 IOPS) or PL2 (100,000 IOPS).
40 GiB system disk is relatively small for AI/ML workloads — you may want to add a data disk or increase the system disk size if you plan to store large datasets or model weights.

OS & Security

Parameter	Value
Image	Ubuntu 24.04 64-bit (Security Hardened)
Login Credential	Key Pair (not yet selected)
Instance Release Protection	Disabled
Metadata Access Mode	Standard + Enhanced mode
Tags	None bound

Overall Assessment

Strengths:

Solid GPU compute instance for AI inference, model serving, image/speech recognition
Ubuntu 24.04 with security hardening is a good baseline for production workloads
Pay-As-You-Go is flexible for testing and variable workloads

Potential Concerns:

System disk is only 40 GiB — this is tight for most AI/ML use cases; consider expanding or adding a data disk
PL0 disk performance may be a bottleneck for I/O-intensive tasks
Key pair not selected yet — make sure to configure this before launch to avoid being locked out
No instance release protection — consider enabling this for production instances to avoid accidental deletion
Default security group — review and restrict inbound rules before exposing to the internet

References:

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