together-gpu-clusters

name	together-gpu-clusters
description	On-demand and reserved GPU clusters (H100, H200, B200) on Together AI with Kubernetes or Slurm orchestration, shared storage, credential management, and cluster scaling for ML and HPC jobs. Reach for it when the user needs multi-node compute or infrastructure control rather than a managed model endpoint.

Together GPU Clusters

Overview

Use Together AI GPU clusters when the user needs infrastructure control instead of a managed inference product.

Typical fits:

• distributed training
• multi-node inference
• HPC or Slurm workloads
• custom Kubernetes jobs
• attached shared storage and cluster lifecycle management

When This Skill Wins

• Provision a cluster and manage it over time
• Choose between on-demand and reserved capacity
• Choose Kubernetes or Slurm as the orchestration layer
• Manage shared volumes and credentials
• Scale up, scale down, or troubleshoot node health

Hand Off To Another Skill

• Use together-dedicated-model-inference for managed single-model hosting
• Use together-dedicated-containers for containerized inference without owning the full cluster
• Use together-sandboxes for short-lived remote Python execution
• Use together-fine-tuning for managed training jobs instead of raw cluster operations

Quick Routing

• Current regions, instance types, pricing, or a non-creating command plan
  • Read references/pricing-and-discovery.md
  • Query the live regions endpoint, then compare only the matching numeric rates on the official pricing table
• Cluster creation, scaling, credentials, deletion
  • Start with scripts/manage_cluster.py or scripts/manage_cluster.ts
  • Read references/api-reference.md
• Shared storage lifecycle
  • Use scripts/manage_storage.py
  • Read references/api-reference.md
• Kubernetes vs Slurm operations
  • Read references/cluster-management.md
• Troubleshooting node health, PVCs, or scheduling
  • Read references/cluster-management.md
• Together CLI workflows
  • Read references/cli.md

Workflow

1. Decide whether the workload really needs cluster-level control.
2. Choose on-demand vs reserved billing based on run duration and baseline utilization.
3. Choose Kubernetes vs Slurm based on orchestration requirements and team tooling.
4. Select region, GPU type, driver version, and shared storage plan.
5. Provision first, then layer in access credentials, workload deployment, scaling, and health checks.

High-Signal Rules

• Python scripts require the Together v2 SDK (together>=2.0.0). If the user is on an older version, they must upgrade first: uv pip install --upgrade "together>=2.0.0".
• Prefer managed products unless the user explicitly needs raw infrastructure control.
• Treat storage lifecycle separately from cluster lifecycle; volumes can outlive clusters.
• When creating a cluster with new shared storage, prefer inline shared_volume over creating a volume separately and attaching via volume_id. Separately created volumes may land in a different datacenter partition than the cluster, causing a "does not exist in the datacenter" error even when the volume shows as available.
• GPU stock-outs (409 "Out of stock") are common. Always call list_regions() first and be prepared to try multiple regions.
• The regions response reports supported configurations, not prices or guaranteed stock. Never infer the cheapest GPU from response order or hardware generation; open the GPU Cluster pricing table and compare its numeric on-demand rates.
• GPU clusters have an 8-GPU minimum. For an hourly estimate, multiply the displayed per-GPU-hour rate by at least 8.
• ON_DEMAND has no one-hour duration flag. For a one-hour plan, omit --duration-days, create the cluster only when authorized, then delete it after the intended runtime. If the user requests a command without provisioning, print it but do not run it.
• The API requires cuda_version and nvidia_driver_version as separate fields in addition to the combined driver_version string. Pass them via extra_body in the Python SDK.
• Credentials retrieval is part of provisioning. Do not stop at cluster creation if the user needs to run workloads immediately.
• Slurm and Kubernetes operational patterns differ materially; read the cluster-management reference before improvising.
• For repeated cluster operations, start from the scripts instead of rebuilding request shapes.
• Slurm startup scripts (worker/login init, worker/controller prolog and epilog, extra slurm.conf) are Slinky v1.0 only. A non-zero exit from a worker prolog or epilog drains the node, and calling Slurm commands (squeue, scontrol, sacctmgr) inside any prolog/epilog can deadlock the scheduler.

Resource Map

• Cluster API reference: references/api-reference.md
• Operational guide: references/cluster-management.md
• Operational troubleshooting: references/cluster-management.md
• CLI guide: references/cli.md
• Live inventory, pricing, and non-creating plans: references/pricing-and-discovery.md
• Python cluster management: scripts/manage_cluster.py
• TypeScript cluster management: scripts/manage_cluster.ts
• Python storage management: scripts/manage_storage.py

Official Docs

• GPU Clusters Overview
• GPU Clusters Quickstart
• Clusters API
• GPU Cluster Pricing
• Slurm Startup Scripts
• Instant GPU Clusters