together-gpu-clusters

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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.

2installs

Sourcetogethercomputer/skills

Added on2026-04-05

NPX Install

npx skill4agent add togethercomputer/skills together-gpu-clusters

SKILL.md Content

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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-endpoints
```
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

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
tcloud CLI workflows
- Read references/tcloud-cli.md

Workflow

Decide whether the workload really needs cluster-level control.
Choose on-demand vs reserved billing based on run duration and baseline utilization.
Choose Kubernetes vs Slurm based on orchestration requirements and team tooling.
Select region, GPU type, driver version, and shared storage plan.
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 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.

Resource Map

Cluster API reference: references/api-reference.md
Operational guide: references/cluster-management.md
Operational troubleshooting: references/cluster-management.md
CLI guide: references/tcloud-cli.md
Python cluster management: scripts/manage_cluster.py
TypeScript cluster management: scripts/manage_cluster.ts
Python storage management: scripts/manage_storage.py