Inference
Distribute model execution across GPUs. Access leading models through one familiar API.
Explore inference
The world isyour engine.
Find the right compute at the right price. Coordinate GPUs into the system your workload needs, from sharded inference to training and evaluation.
Many GPUs.
One coordinated system.
Match price, memory and connectivity to the work. Then coordinate model shards, training workers or evaluation tasks across the right GPUs.
One model
Split the model across a connected GPU group.
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Formal Engines
Orchestration layer
Model memory · connectivity · priceGPU 01Model shard 1
GPU 02Model shard 2
GPU 03Model shard 3
GPU 04Model shard 4
Coordinated model execution
One response
Each GPU holds part of the model. The group works together to serve the request.
Illustrative layouts. GPU grouping depends on workload and connectivity.
There are no bad GPUs,
only bad prices.
The same GPU can carry very different prices. We compare eligible offers and place your workload where the economics make sense.
Memory, capacity and connectivity still have to fit. Once they do, why pay more?
Same GPU. Different prices.
H100 80GB$2.18Lowest eligible offer$3.28Highest eligible offer
Nordics
$2.18
Canada
$2.34
US-central
$2.55
US-east
$2.90
EU-west
$3.12
US-east
$3.28
34% less
for the same GPU model.
$1.10 saved per GPU-hour in this example.
One layer coordinates
the whole run.
From a workload request to its final record, the coordination stays with the work.
Describe
Set the workload, required resources and spending limits.
Place
Match eligible compute to the execution layout the workload requires.
Coordinate
Launch the work and track the GPUs and workers that run it.
Record
Keep placement, price and run state with the result.
Built on the same engine.
Start with model serving or build a learning loop around your own tasks.
Post-training
Turn task outcomes into training signals, then evaluate the next checkpoint against your definition of success.
Explore post-trainingWhat will you build
with a better engine?
Tell us about your model, your workload, or the task you need it to master.