together-gpu-clusters

Provision GPU clusters on Together AI with Kubernetes or Slurm orchestration.

2|1|Updated Feb 20, 2026

Search Tags:#mlops #infrastructure #hpc #distributed-training #together-ai #gpu-clusters

Ready to run

npx skills add https://github.com/zainhas/togetherai-skills --skill together-gpu-clusters

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: together-gpu-clusters
Source: https://github.com/zainhas/togetherai-skills/tree/main/skills/together-gpu-clusters
Command: npx skills add https://github.com/zainhas/togetherai-skills --skill together-gpu-clusters

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires together, together-ai, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Provision GPU clusters on Together AI for distributed training and HPC workloads.

Core Features & Use Cases

Hardware: NVIDIA H100, H200, B200, L40, RTX-6000 with Kubernetes or Slurm orchestration.
Orchestration: Kubernetes or Slurm, autoscaling, health checks, and multi-node management.
Management & Accessibility: Python SDK, TypeScript SDK, tcloud CLI, Terraform, SkyPilot, or REST API.
Storage & Networking: Shared volumes with InfiniBand/nvme-backed storage for high-throughput ML pipelines.
Use Case: Run large-scale distributed training, batch inference, and HPC simulations with on-demand or reserved capacity.

Quick Start

Create an on-demand Kubernetes GPU cluster by following the Python/TypeScript examples to specify region, GPU type, and cluster size.

Frequently Asked Questions about together-gpu-clusters

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I provision GPU clusters on Together AI for distributed training?▼

Provision GPU clusters on Together AI by using the Python or TypeScript SDKs to specify your region, GPU type, and cluster size. You can also use the tcloud CLI, Terraform, SkyPilot, or REST API.

What GPU hardware and orchestration options are available for HPC workloads on Together AI?▼

Together AI HPC workloads support NVIDIA H100, H200, B200, L40, and RTX-6000 GPUs. You can orchestrate your clusters using either Kubernetes or Slurm, enabling autoscaling, health checks, and multi-node management.

Can I use Terraform or SkyPilot to manage on-demand GPU capacity for ML pipelines?▼

Yes, you can manage on-demand GPU capacity for ML pipelines using Terraform or SkyPilot. The platform also supports the tcloud CLI, REST API, and Python/TypeScript SDKs for cluster management and accessibility.

Does Together AI support shared storage with InfiniBand for high-throughput ML pipelines?▼

Yes, Together AI supports shared volumes with InfiniBand and nvme-backed storage for high-throughput ML pipelines. This enables scalable multi-node distributed training and batch inference.

What is the best way to run large-scale distributed training on Together AI?▼

The best way to run large-scale distributed training is provisioning on-demand or reserved GPU clusters via Together AI. You can orchestrate multi-node management and autoscaling using Kubernetes or Slurm.

What are the limitations of using Together AI for batch inference and HPC simulations?▼

Together AI batch inference and HPC simulations rely on available on-demand or reserved capacity for specified NVIDIA GPU types. Users must manage cluster orchestration through provided tools like Kubernetes or Slurm.