Remote & hybrid teams
Engineers get the same GPU workstation at home, in the office, or on the road — the hardware never moves, only the session does.
Private GPU cloud for enterprises
Secure GPU workstations for your team, anywhere.
NodeHive turns GPUs you already own into a private cloud. Engineers get on-demand workstations wherever they work; IT keeps the hardware, the data, and the spend.
Or start a free trial on your own hardware.
- mTLS encrypted
- Role-based access
- Audit logged
- Your hardware
Built for IT
Enterprise-grade from day one
The controls your security review asks about are the foundation, not the roadmap.
Security in depth
mTLS on every connection, hashed credentials, role-based access, and a full audit trail of who did what, when.
Reliable by design
Health checks, metrics, and observability are built into the control plane — not bolted on after an incident.
Your hardware, your data
Workloads run on machines you own, inside your network. Code, models, and data never leave your perimeter.
Spend under control
Budgets, credits, and per-department chargeback show exactly where every GPU hour goes.
How it works
From your rack to a remote engineer in four steps
One control plane connects the GPUs you own to the people who need them.
IT provisions the fleet
Install the agent on the GPU machines you own. They join the control plane over mTLS and show up in the fleet in minutes.
Employees connect securely
Engineers pick a template and get an SSH or JupyterLab workstation on demand — no tickets, no VPN gymnastics, from anywhere.
Work runs on your GPUs
Training, inference, notebooks — isolated per user and scheduled on your hardware, inside your network.
IT sees usage and cost
Live utilization, budgets, and per-department chargeback. Every GPU hour is attributed, every action audit-logged.
Use cases
One fleet, every way your team works
The same private cloud covers the everyday and the exceptional.
Lockdown continuity
When the office is unreachable, work isn't. The fleet keeps serving secure sessions wherever your people are.
Contractors, safely
Give external collaborators a workstation, not your data. Access is scoped, time-boxed, and audit-logged end to end.
Workstation replacement
Stop shipping ₹4-lakh laptops. Any thin client becomes a CUDA workstation backed by the GPUs in your rack.
Burst capacity
Deadline crunch? Pool idle GPUs across departments and burst where the work is — with chargeback keeping it fair.
For platform teams
An API your devops team will actually use
The console is optional. Every provision, stop, and usage query is a REST call — automate rollout the way you automate everything else.
Keys & service accounts
Personal keys for engineers, service accounts for automation — both with TTLs and revocation.
Provision from CI
Everything the console does is a REST call away: create, stop, and inspect workloads from your pipelines.
One-line agent install
New GPU machine? One command enrolls it into the fleet over mTLS.
rollout.sh
# Mint a key in Settings → API access
$ export NODEHIVE_KEY="nh_********"
# Provision a GPU workstation for an engineer
$ curl -X POST "$NODEHIVE_API/api/v1/workloads" -H "Authorization: Bearer $NODEHIVE_KEY" -d '{"name":"ml-dev","template":"pytorch-jupyter","gpu_count":1}'
{"id":"wl_8c2e","status":"pending"}
# Seconds later
$ curl "$NODEHIVE_API/api/v1/workloads/wl_8c2e"
{"status":"running","ssh":"ssh dev@ws-8c2e"}
What rollout looks like
Self-service speed, with the meter running
GPU access stops being a queue and starts being a utility — measured, attributed, and visible.
Illustrative figures — real platform data lands here
- <60s
- from request to a running workstation
- 100%
- of compute on hardware you own
- 0
- tickets filed to get a GPU
- 4 steps
- from rack to remote engineer
Fleet utilization
FAQ
Answers for your security review
The questions IT and platform leads ask first.
Yours. NodeHive is the control plane; compute stays on GPU machines you own, inside your network. Code, models, and data never leave your perimeter.
They pick a template (PyTorch, TensorFlow, CUDA, JupyterLab) and get an SSH or notebook workstation on demand. Sessions work the same from home, the office, or anywhere else.
Every connection runs over mTLS, credentials are hashed, access is role-based, and every action lands in the audit log. Contractor access can be scoped and time-boxed.
Yes — usage is metered per user, project, and department, with budgets, credits, and chargeback built in. Finance gets attribution; engineers get autonomy.
No. Workstations are standard Linux environments with GPU passthrough — your existing frameworks, containers, and editors work as-is.
Installing the agent enrolls a machine in minutes; most teams have their first workstations running the same day. Rollout is per-node, so you can start with one rack.
Give your team GPUs that travel with them.
Your hardware, their laptops, zero compromise. See NodeHive on your own fleet — most teams are running the same day.
Free on up to 2 GPUs — no card, no procurement cycle.