Workspace Types & Hardware
Workspace Types
Linux Workspaces
| Type | Description | Best For |
|---|---|---|
| Amazon Linux 2 | General-purpose Linux environment | Command-line work, scripting, development |
| Ubuntu | Popular Linux distribution with extensive package support | Data science, development, general research |
| RHEL | Enterprise Linux with long-term support | Production workloads, enterprise software |
Windows Workspaces
| Type | Description | Best For |
|---|---|---|
| Windows Server | Windows environment with remote desktop access | Windows-only software, GUI applications |
Specialized Workspaces
| Type | Description | Best For |
|---|---|---|
| SageMaker | Jupyter notebook environment for ML | Machine learning, data science |
| EMR | Big data processing cluster | Large-scale data processing |
Hardware Configurations
General Purpose
| Configuration | vCPUs | Memory | Use Case |
|---|---|---|---|
| Small | 2 | 4 GB | Light development, testing |
| Medium | 4 | 16 GB | Standard analysis, development |
| Large | 8 | 32 GB | Moderate computational work |
| X-Large | 16 | 64 GB | Heavy computational work |
Compute Optimized
| Configuration | vCPUs | Memory | Use Case |
|---|---|---|---|
| Compute Medium | 8 | 16 GB | Parallel processing |
| Compute Large | 16 | 32 GB | High-performance computing |
| Compute X-Large | 36 | 72 GB | Intensive simulations |
Memory Optimized
| Configuration | vCPUs | Memory | Use Case |
|---|---|---|---|
| Memory Medium | 4 | 32 GB | Large dataset analysis |
| Memory Large | 8 | 64 GB | In-memory processing |
| Memory X-Large | 16 | 128 GB | Very large datasets |
GPU Enabled
| Configuration | vCPUs | Memory | GPU | Use Case |
|---|---|---|---|---|
| GPU Small | 4 | 16 GB | 1x NVIDIA T4 | ML inference, light training |
| GPU Medium | 8 | 32 GB | 1x NVIDIA V100 | ML training |
| GPU Large | 32 | 128 GB | 4x NVIDIA V100 | Deep learning, large models |
Warning
GPU instances have limited availability and higher costs. Request GPU access only when your workload specifically requires GPU acceleration.
Storage Options
| Storage Type | Default | Maximum | Notes |
|---|---|---|---|
| Root Volume (SSD) | 50 GB | 500 GB | Operating system and applications |
| Data Volume | Optional | 1 TB | Additional storage for datasets |
Choosing the Right Configuration
| Factor | Guidance |
|---|---|
| Workload Type | Development → Small/Medium; Data analysis → Medium/Large; Simulations → Compute optimized; ML → GPU |
| Data Size | < 10 GB → Standard root volume; 10-100 GB → Increase root volume; > 100 GB → Add data volume |
| Duration | Short tasks → Larger instance for speed; Long-running → Smaller instance for cost |
| Budget | Larger instances cost more per hour; balance size vs. duration |
Tip
Start with a smaller configuration and scale up if needed. You can always terminate and create a new workspace with different specifications.