c9gd.xlarge by Amazon Web Services
(All-cores)
(Single-core)
Specifications
Server Metadata
Vendor ID | aws |
Name | c9gd.xlarge |
Description | Compute optimized [AWS Graviton processors] [Instance store volumes] Gen9 xlarge |
Family | c9gd |
Hw Virt | - |
Status | active |
Observed At | 2026-07-22T03:50:18.720568 |
Availability
| REGION / ID | SPOT | ONDEMAND |
|---|---|---|
Ohio (US) / us-east-2 | 0.056 USD/h | 0.2136 USD/h |
Northern Virgina (US) / us-east-1 | 0.0715 USD/h | 0.2136 USD/h |
Oregon (US) / us-west-2 | 0.0778 USD/h | 0.2136 USD/h |
Frankfurt (DE) / eu-central-1 | 0.1179 USD/h | 0.247 USD/h |
Processor
vCPUs | 4 |
Hypervisor | nitro |
CPU Allocation | Dedicated |
CPU Cores | 4 |
CPU Speed | 2.8 GHz |
CPU Architecture | arm64 |
CPU Manufacturer | AWS |
System Resources and Accelerators
| MEMORY | |
|---|---|
Memory Amount | 8 GiB |
| GPU | |
|---|---|
GPU Count | 0 |
GPU Memory Min | 0 MiB |
GPU Memory Total | 0 MiB |
GPUs |
| STORAGE | |
|---|---|
Storage Size | 237 GB |
Storage Type | nvme ssd |
Storages |
|
| NETWORK | |
|---|---|
Network Speed Baseline | 2.1 Gbps |
Network Speed Max | 15 Gbps |
Network Storage Speed Baseline | 1.5 Gbps |
Network Storage Speed Max | 12 Gbps |
Inbound Traffic | 0 GB/month |
Outbound Traffic | 0 GB/month |
IPv4 | 0 |
Server Description
An arm64 compute-optimized instance featuring dedicated Graviton processors, balanced memory, and high-speed local NVMe SSD storage for efficient batch workloads.
Amazon Web Services c9gd.xlarge is a compute-optimized instance featuring an arm64 CPU architecture powered by AWS Graviton processors. The instance is configured with 4 dedicated physical cores and 4 vCPUs operating at 2.8 GHz, supported by the AWS Nitro hypervisor. It includes 8.0 GB of system memory, yielding a 2.0 GB per core ratio. For local storage, the instance is equipped with a 237 GB NVMe SSD instance store volume, offering high-speed local storage capabilities. Networking is provisioned with a baseline bandwidth of 2 Gbps. This hardware profile provides qualitative cost efficiency for compute-bound workloads that require fast local scratch space. The c9gd.xlarge is designed for workloads such as batch processing, video encoding, ad serving, and containerized microservices.
Economics
Alternatives
Servers of the Same Family
| INSTANCE | vCPUs | MEMORY | GPUs |
|---|---|---|---|
| c9gd.medium | 1 | 2 GiB | 0 |
| c9gd.large | 2 | 4 GiB | 0 |
| c9gd.2xlarge | 8 | 16 GiB | 0 |
| c9gd.4xlarge | 16 | 32 GiB | 0 |
| c9gd.8xlarge | 32 | 64 GiB | 0 |
| c9gd.12xlarge | 48 | 96 GiB | 0 |
| c9gd.16xlarge | 64 | 128 GiB | 0 |
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