Everything you need to train, deploy, and scale AI
Build, train, and deploy AI models faster. From experimentation to production, Muon gives you the infrastructure to ship AI products without managing complexity.
GPU Pods
On-demand GPU instances with Jupyter, web terminal, and persistent storage.
Learn more Scale to zeroServerless
Auto-scaling inference that scales to zero. Deploy via Docker or templates.
Learn more PersistentStorage
Persistent file system and block storage. Mount to any workload.
Learn moreDedicated compute for development & training
Isolated, production-ready execution environments for ML workloads. Provision GPU or CPU resources on demand with full control over your environment.
Latest NVIDIA GPUs on demand
Jupyter, PyTorch, TensorFlow ready
Browser notebook, web terminal
Data persists across restarts
name, memory.total [MiB]
NVIDIA A100-PCIE-80GB, 81920 MiB
✓ Loading model checkpoint...
✓ Dataset loaded: 50,000 samples
✓ Training started on GPU 0
Epoch 3/10 | Loss: 0.0234 | LR: 1e-4
Active Workers
12
Requests/min
2.4k
Avg Latency
45ms
Auto-scaling inference that scales to zero
Deploy AI models that scale automatically. Compute is provisioned per request and scales to zero when idle—optimized for inference and on-demand workloads.
Docker images, GitHub repos, templates
Scale to thousands, back to zero
Only charged for active compute time
Invocation logs, latency, metrics
Persistent storage for all your workloads
First-class storage primitives that integrate seamlessly with pods and serverless. Your datasets, models, and artifacts—always available.
Fast storage for single-pod workloads
Shared storage across multiple pods
Works with pods and serverless
Data stays when pods restart
ml-datasets-vol
500 GB • NVMe SSD
342 GB used
Connected workloads
Zero to working ML environment in minutes
Fast, reproducible ML environments under one control plane.
Choose your compute
GPU pods for development and training, or serverless for inference workloads.
Deploy your workload
Use prebuilt environments, deploy from Docker/GitHub, or select a model template.
Start working
Access via Jupyter, web terminal, API, or SDK. Monitor with built-in observability.