ecs.ebmgn8ia.64xlarge by Alibaba Cloud
(All-cores)
(Single-core)
Specifications
Server Metadata
Vendor ID | alicloud |
Name | ecs.ebmgn8ia.64xlarge |
Description | ecs.ebmgn8ia family (256 vCPUs, 1536 GiB RAM, 0 GB storage, 4xL20 49152 GiB VRAM) |
Family | ecs.ebmgn8ia |
Hw Virt | - |
Status | active |
Observed At | 2026-10-24T15:16:59.109204 |
Availability
| REGION / ID | SPOT | ONDEMAND |
|---|
Processor
vCPUs | 256 |
Hypervisor | KVM |
CPU Allocation | Dedicated |
CPU Speed | 3.4 GHz |
CPU Architecture | x86_64 |
CPU Manufacturer | AMD |
CPU Family | EPYC |
CPU Model | 9T34 |
System Resources and Accelerators
| MEMORY | |
|---|---|
Memory Amount | 2 TB |
| ACCELERATORS | |
|---|---|
Accelerator Count | 4 |
Accelerator Memory Min | 48 GiB |
Accelerator Memory Total | 192 GiB |
Accelerator Type | GPU |
Accelerator Manufacturer | NVIDIA |
Accelerator Family | Ada Lovelace |
Accelerator Model | L20 |
Accelerators |
| STORAGE | |
|---|---|
Storage Size | 0 GB |
Storages |
| NETWORK | |
|---|---|
Network Speed Baseline | 160 Gbps |
Inbound Traffic | 0 GB/month |
Outbound Traffic | 0 GB/month |
IPv4 | 0 |
Server Description
A high-density GPU instance featuring AMD EPYC processors and four NVIDIA L20 accelerators for parallel computing and data science workloads.
Alibaba Cloud ecs.ebmgn8ia.64xlarge is an x86_64 bare-metal server powered by AMD EPYC 9T34 processors, featuring 256 dedicated vCPUs running at 3.4 GHz and 1536.0 GB of system memory. The instance is equipped with four NVIDIA L20 GPUs from the Ada Lovelace architecture, delivering 192 GB of total VRAM to accelerate parallel computing tasks. It features a baseline network bandwidth of 160 Gbps but lacks local storage, requiring external network-attached storage solutions. This high-density hardware configuration is designed for demanding workloads such as machine learning inference, large-scale data science, and high-performance computing. The combination of massive CPU cores and dedicated GPU acceleration provides a robust platform for parallel processing without the overhead of local storage management.
Economics
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