Cloud GPU Configurations Skill

Platform-specific configuration templates and GPU selection guidance for Modal, Lambda Labs, and RunPod cloud platforms.

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name: cloud-gpu-configs description: Platform-specific configuration templates for Modal, Lambda Labs, and RunPod with GPU selection guides allowed-tools: Bash, Read, Write, Edit

Use when:

• Configuring cloud GPU platforms for ML training
• Selecting appropriate GPU types for workloads
• Setting up Modal, Lambda Labs, or RunPod environments
• Optimizing cost vs performance for GPU compute
• Generating platform-specific configuration files

GPU Selection Guide

Modal GPUs

Available GPUs: T4, L4, A10, A100 (40GB/80GB), L40S, H100/H200, B200

Selection Criteria:

• T4: Budget-friendly, light inference ($0.20-0.40/hr)
• L4: Modern alternative to T4, better performance
• A10: Good all-around option, up to 4 GPUs (24GB VRAM)
• L40S: Excellent cost/performance for inference (48GB VRAM)
• A100: Training standard, 40GB or 80GB variants
• H100/H200: Cutting-edge performance, best software support
• B200: Latest Blackwell architecture, premium performance

Multi-GPU Support:

• T4, L4, L40S, A100, H100, H200, B200: Up to 8 GPUs
• A10: Up to 4 GPUs

Lambda Labs GPUs

Available Instances:

• 1x A100 (40GB): Standard training, $1.10/hr
• 1x A100 (80GB): Large models, $1.29/hr
• 8x A100 (40GB): Distributed training, $8.80/hr
• 8x A100 (80GB): Large-scale training, $10.32/hr
• 1x H100: Latest generation, $2.49/hr
• 8x H100: Maximum performance, $19.92/hr

Selection Criteria:

• Single A100: Most common training workload
• 8x A100: Multi-node distributed training
• H100: When cutting-edge performance needed

RunPod GPUs

Available GPUs: RTX 3090, RTX 4090, A4000, A5000, A6000, A40, A100, H100

Pricing Models:

• Spot Instances: 50-80% cheaper, can be interrupted
• On-Demand: Guaranteed availability, higher cost

Selection Criteria:

• RTX 3090/4090: Consumer GPUs, excellent price/performance for small models
• A4000/A5000: Professional GPUs, stable for production
• A6000: 48GB VRAM, large model training
• A100: Industry standard, 40GB or 80GB
• H100: Premium performance

Usage

Setup Modal Environment

bash scripts/setup-modal.sh

Prompts for:

• Modal token
• Default GPU type
• Python version

Creates:

• modal_image.py - Configured Modal image
• .modal_config - Environment configuration

Setup Lambda Labs Environment

bash scripts/setup-lambda.sh

Prompts for:

• Lambda API key
• SSH key path
• Preferred instance type

Creates:

• lambda_config.yaml - Instance configuration
• SSH configuration

Setup RunPod Environment

bash scripts/setup-runpod.sh

Prompts for:

• RunPod API key
• GPU type preference
• Spot vs on-demand

Creates:

• runpod_config.json - Pod configuration
• Environment setup script

Templates

Modal Image Template (templates/modal_image.py)

Configurable Modal image with:

• GPU selection
• Python dependencies
• System packages
• CUDA configuration

Lambda Config Template (templates/lambda_config.yaml)

Instance configuration with:

• Instance type selection
• SSH key configuration
• Startup scripts
• Volume mounting

RunPod Config Template (templates/runpod_config.json)

Pod configuration with:

• GPU type and count
• Container image
• Volume configuration
• Network ports

Examples

• examples/modal-t4-setup.md - Budget-friendly Modal setup with T4
• examples/lambda-a10-setup.md - Standard Lambda Labs A100 configuration

Cost Optimization Tips

Modal

• Use L40S for inference (best cost/performance)
• Enable automatic upgrades (H100 → H200) for no extra cost
• Use GPU fallback for faster scheduling
• Avoid requesting >2 GPUs unless necessary

Lambda Labs

• Single A100 instances are most cost-effective for training
• Use persistent storage to avoid re-downloading datasets
• Terminate instances when not in use
• Consider 8x A100 only for true distributed workloads

RunPod

• Use spot instances for interruptible workloads (50-80% savings)
• RTX 4090 offers excellent value for smaller models
• Use on-demand only for production/critical workloads
• Enable auto-shutdown to prevent idle costs

Performance Considerations

Memory-Bound Operations

• Consider total VRAM over compute power
• L40S (48GB) often better than A100 (40GB) for inference
• A100 80GB for large model training

Compute-Bound Operations

• H100/H200 for maximum throughput
• B200 for latest architecture features
• Multiple A100s for distributed training

Multi-GPU Training

• Use PyTorch DDP or DeepSpeed
• Ensure efficient data loading (multiple workers)
• Profile to avoid CPU bottlenecks
• Consider network bandwidth between GPUs

Integration with ML Training

This skill integrates with other ml-training components:

• framework-templates: Provides GPU configs for generated training scripts
• training-orchestrator: Uses these configs for distributed training
• cost-estimator: Uses pricing data for budget planning