neocloud · container · Layer 2.5
JarvisLabs
Individual researchers and small teams, particularly in India.
medium portability Movable with effort. Expect to redo images, storage wiring and networking.
What you are buying
- Business model
- Purpose-built GPU cloud. Owns or leases its own datacentre capacity and sells it directly.
- How you reach it
- You push a container image and it runs. No cluster to operate.
- Accelerators
- A100, H100, RTX 6000 Ada, A6000
- Regions
- India, US
- Pricing model
- Hourly, pause-and-resume billing.
- Getting started
- Very low.
- Capacity
- Small.
- Ownership
- Private.
Verified 2026-09-09. Fields reading “not published” are exactly that — we do not estimate a figure a vendor withholds.
The counterparty
Small provider.
A multi-year GPU commitment is a credit decision wearing a cloud contract. This is the section no benchmark covers and the one that decides what happens to your workload in 2028.
The catch
Small scale. Fine for one machine, not for a cluster.
The economics
Pause-and-resume is the useful feature — you stop paying without losing the environment.
No rate is quoted on this page on purpose. Published list prices at this layer move weekly and essentially nobody signing a real contract pays them. Dated, sourced figures live in the GPU rental price index, where the spread between the cheapest and dearest seller of the identical chip runs to roughly 9x.
The layers underneath this one
Whatever you rent here is a chip in a building that needs power. In 2026 megawatts, not silicon, are the binding constraint on the whole industry — a frontier rack draws 120–200 kW against a 2026 average near 27 kW, and the US interconnection queue exceeds 2,600 GW.
Layer 2 — Silicon · Layer 1 — Energy · The interconnection queue · Tokens per watt
Compared against
- Paperspace (DigitalOcean) vs JarvisLabs — Notebook-first workflows at two very different scales.Compare with Paperspace (DigitalOcean)
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