From idea to GPU in minutes.
Run AI workloads on-demand with no infrastructure complexity. Access GPUs instantly for experiments, fine-tuning, and inference — pay only for what you use.
$ na serverless jobs submit --name llm-finetune
--container-image cr.neutrino.ai/ml/pytorch:2.4
--gpu-cluster h100 --gpu-count 8
--command "python train.py"
✓ Job created
✓ GPUs provisioned in 47s
✓ Container pulled & started
✓ Training running
GPU power
without the overhead
Skip the weeks of cluster provisioning, driver wrangling, and network configuration. Neutrino AI Serverless gives you the power of dedicated GPU infrastructure with the simplicity of a single CLI command.
Run AI in minutes
Run GPU workloads without waiting for clusters to be provisioned, configured and validated. Submit a job and it runs — instantly.
No infrastructure overhead
No cluster setup, drivers, network configuration or orchestration required. Neutrino AI handles all of it so your team stays focused on AI.
Pay only for what you use
Pay only while workloads are running — no idle GPU costs, no reserved commitments, no surprise bills. Billed to the second.
Scale instantly when needed
Provision additional compute on demand when workloads spike. Scale down to zero when they finish. Always right-sized, always available.
Three services, every stage of AI
Neutrino AI Serverless provides three services that support different stages of the AI workflow — from exploratory prototyping to production inference.
Jobs
Runtime for executing containerized finite workloads that start, run and complete.
Endpoints
Inference environment for deploying custom models behind scalable HTTP endpoints.
DevPods
Interactive GPU development environments with Jupyter and VS Code preinstalled.
Getting started
Contact us to request large-scale GPU clusters, or sign up to the Neutrino AI Cloud (Powered by Nebius) console to deploy GPUs immediately.
Serverless AI complements Neutrino AI's high-performance clusters
Serverless AI is designed to work alongside Neutrino AI's core offering of dedicated GPU clusters for large-scale training — covering every phase from idea to production.
Prototype ideas and debug code in DevPods. Spin up a Jupyter or VS Code environment with GPU in seconds — no setup, no waiting.
DevPodsRun training experiments or batch processing with Jobs. Iterate fast on hyperparameters and architecture choices without committing to a cluster.
JobsRun full training, fine-tuning and simulation workloads with Jobs. Scale to hundreds of GPUs on demand — scale to zero when done.
JobsValidate your inference pipeline and serve models through Endpoints. Auto-scaling HTTP APIs with built-in load balancing and health checks.
Endpoints