GPU infrastructure that builders actually want.
Run inference, training, and batch jobs with sub-second cold starts, instant autoscaling, and per-second on-chain billing. No KYC. No sales calls.
npm i -g robingpurobin connectrobin deploy job.yamlHardware available on demand
Wallet to workload in four steps.
No forms, no approvals, no onboarding calls. The whole flow takes less than a minute.
Connect a wallet
Your wallet address is your account. No email, no password, no identity documents - ever.
~5 secondsDeposit crypto
Fund your balance with stables or native assets. It converts to compute credit instantly.
~20 secondsDefine your job
One file: GPU type, count, container image, command. Your repo is the source of truth.
~2 minutesDeploy and monitor
Push it. Cold start in under a second, live logs streaming, cost ticking per second on-chain.
0.8s cold startThe production cloud for GPU workloads.
Everything between your code and the hardware, handled. Define the job, push it, watch it run.
Your cloud environment, in one file.
Container image, hardware spec, and job logic live together in your repo. No dashboards to babysit.
gpu: h100 · count: 4 image: pytorch/pytorch:latest billing: per-second $ robin deploy → live in 0.8s
Cold starts under a second.
Containers boot instantly from a warm image cache. Serving before a traditional instance finishes provisioning.
Autoscale 0 → hundreds of GPUs.
Burst when traffic hits, drop to zero when it doesn't. Pay for seconds of actual compute - nothing idle.
Every job. Every second. Every cent.
Live logs, GPU utilization, and per-job cost tracking built in. Know exactly what ran and what it cost.
[00:01] gpu util 94% · vram 61/80GB [00:02] req/s 212 · p95 41ms [00:03] cost so far $0.0311 [00:04] autoscale +2 replicas
Build full-scale AI systems.
One platform for the four workloads that eat GPU hours.
Inference
Serve LLMs, image gen, and audio models. Token streaming and websockets out of the box. Scale to zero between requests.
Training
Fine-tunes, LoRA, or full runs. Single GPU to multi-node clusters with one config change.
Batch jobs
Embeddings, evals, dataset generation. Thousands of parallel workers with zero orchestration code.
Agent sandboxes
Isolated, ephemeral environments for untrusted or autonomous code. Spin up thousands, tear down instantly.
Pay in crypto. Start in seconds.
- No credit card, no invoice, no identity check
- Connect a wallet, deposit, compute
- Balance streams down per second while jobs run
- Top up any time, withdraw the rest whenever
- Every charge visible and verifiable on-chain
Built different, on purpose.
Traditional GPU clouds were designed for enterprises with procurement teams. We designed for builders with wallets.
Speed as the default
Sub-second cold starts and instant autoscaling. Your infrastructure keeps pace with your iteration speed, not the other way around.
Radical cost transparency
Per-second billing settled on-chain. Every charge is visible, verifiable, and stops the moment your job does. Zero idle spend.
Zero-friction access
No KYC, no sales pipeline, no capacity requests. A wallet and a deposit are the entire onboarding. Withdraw unused credit anytime.
Per-second pricing. No tiers, no games.
The price on the card is the price you pay. Billed by the second, settled on-chain.
Questions, answered straight.
No. Connect a wallet and deposit - that's the whole signup flow. Your wallet address is your identity on the platform.
Your deposited balance streams down for every second a job runs. The moment a job stops, billing stops. Every charge is recorded and verifiable on-chain.
Major stables and native chain assets. Deposits convert to platform credit at the current rate, and unused credit can be withdrawn at any time.
Yes. Every job runs in its own hardware-isolated container. Nothing is shared between tenants, and environments are destroyed on teardown.
Yes. Request the GPU count in your job spec and the scheduler gang-allocates the cluster. Same file, same command, more hardware.