NEW

Anvil 192 clusters are live in Amsterdam →

Train bigger models.
Wait less.

Ferrite gives your team fabric-connected GPU clusters in about 90 seconds, billed by the second. No reservations, no quotas, no three-week sales cycle.

ams-1 · hall B · 384 GPUs

ams-1 · 384 GPUs

live

Utilisation

87

%

Jobs running

21

jobs

Just started

train · vision-7b

64 GPU

finetune · support-bot

8 GPU

eval · coder-34b

16 GPU

train · protein-fold

128 GPU

serve · search-rerank

4 GPU

Training on Ferrite

Northwind AI

Lumen Bio

Okapi

Fathom Labs

Quill

Meridian Health

Why Ferrite

The boring parts, done properly

Fast hardware is the easy bit. We spend our time on the networking, storage and scheduling that decide whether a run finishes on Tuesday or next month.

A long, bright aisle between rows of server racks

Our own halls, our own racks

We design and run every data hall ourselves, so a cluster you launch today is the same cluster next month.

90s

From click to cluster

Median time to a ready 64-GPU cluster, measured over the last 30 days.

3.2 Tb/s between every GPU

Non-blocking fabric across the whole hall, so 512 GPUs behave like one machine.

A bundle of glowing blue fibre-optic cables

Fibre to the rack

Private links to your cloud and on-prem storage.

Storage that keeps up

Parallel file system at 40 GB/s per node, with checkpoints that save in seconds.

Billed by the second

Stop a cluster and the meter stops. No minimum hours, no idle reservations, no surprises.

100% renewable power

Every hall runs on wind and hydro, and the waste heat warms 2,000 homes nearby.

Developer first

Five lines from laptop to 64 GPUs

Use the Python SDK, the CLI or plain HTTP. Bring any container, and your code runs the same as it does on your machine.

Works with PyTorch, JAX and anything in a container

Automatic checkpoints and restarts if a node fails

SSH into any node, any time

Python

CLI

curl

Copy

from ferrite import Cluster

# 64 GPUs, fabric-connected, ready in about 90 seconds
cluster = Cluster.create(gpu=“forge-80”, count=64, region=“ams-1”)

job = cluster.run(
image=“ghcr.io/acme/trainer:latest”,
command=“torchrun train.py --config big.yaml”,
checkpoint=“s3://acme/ckpt/“,
)
job.logs(follow=True)

14,000

GPUs across seven regions

99.98%

cluster uptime last quarter

38%

lower cost than the big three clouds

<4 min

median support response

Portrait of Kenji Mori

“We moved a six-week training run to Ferrite and it finished in nine days. The cluster just stayed up, which is the highest praise I can give infrastructure.”

Kenji Mori

Head of ML, Northwind AI

Start in minutes

Your first 100 GPU-hours are on us

Sign up, add a card and launch a cluster before your coffee’s cold. No sales call, no minimum spend.

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