Launch Templates: Infrastructure Portability for Production AI

AI infrastructure should not dictate how you build models.

Your workloads should dictate how infrastructure adapts.

You've configured a PyTorch environment for distributed training. Tomorrow you need to scale to 20 nodes. Next week, GPU availability shifts and you need to run the same workload on different hardware.

Each change forces reconfiguration. New images. New scripts. New assumptions. New failures.

Not because your models changed — but because your infrastructure did. This is the hidden tax of modern AI systems.

At Yotta Labs, we built Launch Templates to eliminate it.

Define once.

Deploy anywhere.

Scale your portable infrastructure across heterogeneous GPUs without rewriting your stack.The Anatomy of a Launch Template

The Anatomy of a Launch Template

Launch Templates are hardware-agnostic deployment manifests for GPU Pods on Yotta Platform. Every Launch Template captures your complete workload specification:

1. Container Image: Docker image with your framework stack
2. Runtime Commands: Initialization scripts and entry points
3. Environment Variables: API keys, paths, hyperparameters
4. Network Configuration: Port mappings for services and APIs
5. Storage: Volume mounts and persistence requirements
6. Resources: Compute requirements (GPU memory, VRAM, performance tiers), not locked to vendor SKUs

You declare requirements. Yotta's scheduler matches your template to available compute across the network. As prices shift or new silicon joins the platform, workloads flow to the most suitable hardware. No template changes needed.

Three Production Scenarios

1. Adaptive Training Across Hardware

Your distributed training job runs on H100s today. Availability drops tomorrow. Your Launch Template specifies compute requirements, not specific GPUs, so Yotta's scheduler automatically provisions across available A100s or other suitable accelerators. Training continues without reconfiguration.

2. Cost-Optimized Inference at Scale

Your FLUX-1.dev deployment needs 50 Pods. Rather than locking into a single GPU type, your template lets Yotta optimize across the network, mixing consumer GPUs and data center accelerators based on real-time cost and availability. Heterogeneous execution, predictable results.

3. Reproducible Research Without Hardware Lock-In

You're submitting to NeurIPS. Reviewers need to reproduce your results but don't have your exact GPUs. You share your Yotta template. They deploy on whatever hardware is available. The template ensures identical software stack and configuration. Reproducibility without requiring identical silicon.

Official Templates: Optimized for Heterogeneous GPUs

Yotta Labs maintains curated Official Templates engineered for the platform's heterogeneous network:

ML Frameworks: PyTorch 2.9.0/2.8.0, Unsloth, Miles, Skyrl

Generative AI: ComfyUI, FLUX-1.dev, Qwen, Wan 2.2/2.1

Specialized Compute: Crowdcent

These are not examples. They are production baselines.

Private Templates: Codify Your Custom Stack

Official Templates cover standard ML workflows. For custom requirements—proprietary pipelines, internal auth systems, specialized preprocessing—create Private Templates visible only to your team.Create in 6 steps:

1. Navigate to Templates in Yotta Console, click Create
2. Choose base: Official Template or custom Docker image
3. Add environment variables (credentials, paths, hyperparameters)
4. Configure port mappings (JupyterLab, TensorBoard, APIs)
5. Set startup commands and resource requirements
6. Save to your organization's library

Your team now deploys identical Pods with one click. No wiki pages, no tribal knowledge. Templates support versioning, A/B testing, and rollbacks. Infrastructure as code for ML workloads.

Programmatic Deployment via API

The real power of Launch Templates comes from API integration. Infrastructure that deploys itself based on application logic.

Example: Hardware-agnostic training deployment

from yotta import YottaClient 
client = YottaClient(api_key=os.getenv("YOTTA_API_KEY")) 

# Scheduler selects cost-effective GPUs meeting your requirements 
training_pods = client.pods.create_from_template( 
  template_id="private-llama-training-v2", 
  min_gpu_memory="40GB", # Requirements, not specific SKUs 
  count=8, 
  max_price_per_hour=3.50, 
  environment={ 
    "EXPERIMENT_ID": "exp_20250205_001", 
    "LEARNING_RATE": "3e-4" 
  }
)

This enables auto-scaling inference, scheduled batch jobs, and CI/CD integration, all hardware-agnostic and cost-optimized across Yotta's heterogeneous network.

Getting Started

1. Browse the Official Templates library. Deploy in less than 30 seconds
2. Customize for your workflow. Add variables, configure ports, specify resource requirements
3. Save as a Private Template for your organization
4. Integrate via API for automated, hardware-agnostic deployments

Your infrastructure becomes portable, reproducible, and independent of vendor silicon roadmaps. This is interoperability in practice.

As AI accelerators diversify, platforms that treat silicon as a variable, not a prerequisite, will endure. Yotta Labs is building that future.

Deploy portable AI infrastructure: Explore Launch Templates →

Programmatic control: API Documentation →

Yotta Labs democratizes AI infrastructure through an interoperable compute OS, making production workloads portable across clouds and silicon. Learn more →