On-demand GPU cloud

On-demand GPUs for the teams building artificial intelligence.

Rosecut is a cloud computing platform that gives AI teams instant, on-demand access to graphics processing units — without the cost and complexity of buying, racking, and managing their own hardware. Several compute models that usually live on separate platforms are consolidated into a single service, billed per second of compute actually consumed, and built to run any standard container as-is.

At a glance

Metered per second. You pay only for compute you actually use; jobs that finish early are never billed for unused time.
Container-native. Any standard Docker container runs without modification — no proprietary SDKs, no rewrite, no vendor lock-in.
Hosted in Europe. GPUs live in European data centres for in-region data residency and data-protection compliance.
Live in seconds. Capacity provisions in seconds rather than after weeks of procurement, so the first job starts almost immediately.

An accessible, transparent, regionally hosted alternative to general-purpose cloud providers — purpose-built for GPU-intensive AI development and deployment, from a single inference call to a cluster of several thousand interconnected accelerators.

The platform

One platform for every kind of accelerated compute.

Most teams end up stitching together a different vendor for every job — one for inference, another for raw GPUs, a third for big training runs. Rosecut consolidates the whole spectrum into a single service, a single workflow, and a single per-second bill. Bring a container, choose how you want to run it, and the platform handles the hardware underneath.

Underneath that one account sit five distinct compute models: a serverless inference API with pay-per-token access to open-source large language models; dedicated, reserved GPU endpoints for production models that need consistent low latency and high uptime; serverless training that runs your code on GPUs with no infrastructure to manage; on-demand GPU virtual machines with full root access that provision in seconds; and large-scale clusters ranging from a handful to several thousand interconnected GPUs linked by high-bandwidth networking.

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to us

Our compute

Five ways to run on Rosecut.

One platform, five compute models — pick the one that fits the job, or mix them across a project. Each runs your own container, bills per second, and lives in the same European region.

Serverless · Per-token Inference

Serverless inference API

A single endpoint for open-source large language models, billed per token. Send a request, get a completion — the platform loads, batches, and scales the model behind the API, so there is nothing to provision and nothing to keep warm.

Pay-per-token access to a catalog of open-source LLMs
No servers to stand up, autoscale, or keep running
A plain HTTP API that drops into code you already have
Ideal for prototypes, language features, and bursty traffic

Dedicated · Reserved Endpoints

Dedicated GPU endpoints

When a model is in production and latency matters, reserve dedicated GPUs behind a private endpoint. The capacity is yours alone, so response times stay consistent under sustained load instead of competing with anyone else for the hardware.

Reserved GPUs sitting behind your own endpoint
Consistent low latency, even under steady production load
High, predictable uptime for always-on services
Your models served privately, on capacity you control

Serverless · Training Training

Serverless training

Point Rosecut at your training code and it runs on GPUs without you standing up a single piece of infrastructure. Spin up for a fine-tune or a full run, pay for the seconds it actually takes, and the hardware releases the moment the job finishes.

Run training code directly on GPUs, no cluster to manage
Nothing to provision before a run or tear down after
Scales to the size of the job, then releases the hardware
Per-second billing on every experiment and full run

On-demand · Root access Machines

On-demand GPU machines

Sometimes you just want a GPU box. On-demand virtual machines give you full root access and provision in seconds — install what you like, run any container, and shut it down the instant you are done. No tickets, no procurement, no waiting.

Full root access to the whole machine
Provisioned in seconds, not procurement cycles
Bring any container, framework, or custom stack
Pay by the second and stop whenever you like

Clusters · Up to thousands Clusters

Large-scale clusters

For frontier-scale work, compose clusters of interconnected GPUs — from a handful of nodes to several thousand — linked by high-bandwidth networking built for large distributed training and the most demanding models.

From a handful to several thousand interconnected GPUs
High-bandwidth interconnect between every node
Reserved capacity for distributed training at scale
Networking tuned for tightly-coupled multi-node jobs

Not sure which model fits?
Most teams mix several — a serverless API for prototypes, dedicated endpoints in production, and clusters for the big training runs. Tell us about the workload and we will map it to the right compute.

Talk through your workload

How it works

Built for teams who would rather ship models than manage machines.

Procuring GPUs the old way means quotes, lead times, racks, and capacity you have to grow into. Rosecut replaces all of that with one platform you can reach in seconds — and a single design philosophy that runs through every part of it: stay out of the way of the work.

Serverless inference for open-source LLMs, billed per token
Dedicated endpoints for production models that can't wait in a queue
Serverless training that scales to the run, then releases the hardware
On-demand GPU machines with full root access in seconds
Clusters from a handful to several thousand interconnected GPUs
Documentation and practical engineering guides behind all of it

Below are the choices that make Rosecut an accessible, transparent, regionally hosted alternative to the general-purpose clouds.

The platform

What sets Rosecut apart.

A handful of deliberate design decisions — about billing, portability, location, and speed — separate Rosecut from a general-purpose cloud. Here is each one, in full.

Per-second billing

Compute is metered to the second. You pay only for what a job actually consumes, and a run that finishes early is never charged for the time it didn't use — so cost tracks real usage instead of reservations.

Billed per second of real compute
Early-finishing jobs aren't charged for idle time
Spend that follows usage, not commitments

Container-native

Any standard Docker container runs without modification — no proprietary SDKs and no rewrites. What runs on your laptop runs on Rosecut, which is exactly what keeps you free to take it elsewhere.

Standard Docker images, completely unmodified
No proprietary SDKs to adopt or maintain
Portable by design — no vendor lock-in

European data residency

GPUs are hosted in European data centres, keeping your data in-region and aligned with the region's data-protection regulation — built for organizations with real data-sovereignty requirements.

Compute hosted in European data centres
In-region data residency
Aligned with EU data-protection rules

Live in seconds

Capacity provisions in seconds rather than after weeks of procurement, with a sub-minute time to first job. An idea can be running on a GPU almost as fast as you can describe it.

GPUs available in seconds, on demand
Sub-minute time to first job
No procurement and no capacity tickets

Open-model catalog

The inference API spans a catalog of open-source models, so you call open weights directly over a simple API instead of being tied to a single closed provider — and you can switch models as needs change.

A catalog of open-source models for inference
Open weights, called over a plain API
Freedom to switch models without re-platforming

A spectrum of GPUs

Choose from a spectrum of GPU hardware at different memory and price points — from efficient inference cards to the newest, highest-memory accelerators reserved on longer-term contracts for the heaviest training.

Hardware across memory and price points
Right-size to the job instead of overpaying
Newest, highest-memory accelerators available

Operational reliability

The platform is run to keep hardware busy and queues short. Rosecut reports high average GPU utilisation, short queue times, a sub-minute time to first job, and strong platform uptime as the operational baseline.

High average GPU utilisation
Short queue times and fast scheduling
Strong, dependable platform uptime

Docs & guidance

Documentation and practical guides help teams get more from every GPU — covering cost optimization, right-sizing instances, migrating workloads away from the large hyperscalers, and memory management for large language models.

Documentation that gets you to a running job fast
Guides on cost optimization and right-sizing
Help migrating off the big hyperscalers

See it run

Bring a container. Pick your compute. Go.

No proprietary SDK, no rewrite. A standard image, a GPU from the spectrum below, and a meter that only runs while your job does.

rosecut — eu-region metered · per second

$ rosecut run --gpu --container my-model:latest

→ matching capacity in european region …

✓ container accepted — no changes required

✓ live in seconds · full root access

○ job running — you are billed only while this is green

+1s +1s +1s — stops the instant the job does

GPU hardware spectrum

Inference-class GPUsLower memory · efficient

Balanced GPUsTraining & serving

High-memory GPUsLarge models

Newest acceleratorsHighest memory · reserved 3mo+

Three ways to pay

Serverless · per-token On-demand · per-second Reserved · newest accelerators

Rates depend on the hardware and how you reserve it — from per-token serverless inference to per-second on-demand machines and longer-term contracts for the newest, highest-memory accelerators. Tell us the workload and we will share current numbers and right-size it with you.

Get current rates

Operations

Production-grade from the first job.

Rosecut reports its operational baseline as high average GPU utilisation, short queue times, a sub-minute time to first job, and strong platform uptime — the qualities that matter when real workloads depend on the platform.

High GPU utilisation

The fleet is kept busy. High average utilisation means the hardware you pay for is doing real work — not sitting idle behind a reservation.

Short queue times

Jobs schedule quickly. Short queues keep experiments moving and production traffic responsive instead of waiting for a free slot.

Sub-minute first job

From request to running in under a minute. There is almost no gap between deciding to run something and watching it run on a GPU.

Strong uptime

Built to stay up. Strong platform uptime keeps dedicated endpoints answering and long training runs alive when it matters most.

Guides & documentation

Help getting more out of every GPU.

Beyond the platform itself, Rosecut publishes documentation and practical guides on the questions GPU teams actually hit — spending less, sizing right, moving over, and fitting bigger models.

Documentation

Container-native docs that take you from a standard image to a running GPU job — with reference for the inference API, machines, endpoints, and clusters.

Cost optimization

Practical ways to spend less per result — leaning on per-second billing, batching, and matching each job to the cheapest hardware that can run it.

Right-sizing instances

How to pick the GPU that fits the model, so you are not paying for memory and throughput a workload never touches — or starving one that needs more.

Migrating off hyperscalers

A route for moving GPU workloads away from the large general-purpose clouds without rewriting them — because standard containers travel as-is.

Memory for LLMs

Managing memory for large language models — context length, KV-cache, and quantisation — to fit bigger models onto the same card.

Plan a workload →

Want help estimating spend or right-sizing before you commit? Tell us what you are running and we will size it with you and share current rates.

Custom support

Let's get your GPUs running.

Every workload is shaped differently — a bursty inference endpoint, a month-long training run, a cluster booked for a launch. Tell us yours and we will map it to the right compute, in the right region, at the right size. No sales maze; you reach the people who run the platform.

Phone

(956) 521-1579

hello@getrosecut.com

Address

4321 Marmion Way,
Los Angeles, CA 90065

Legal entity

Rosecut LLC

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