Fast starts by design
Model-aware caching and prebuilt layers reduce the startup penalty without forcing always-on capacity.
Serverless GPU
Inference that scales with the request, not the guess.
Turn a container or repository into a production endpoint with smart GPU routing, warm pools, built-in queues and scale-to-zero economics.
Transparent baseline
$0 idle
flex workers scale to zero between requests
<250 ms
Target cold start
0→500
Workers
P98
Latency views
4
Deploy sources
EU regions, encrypted storage and network isolation options
Why GPURento
Less infrastructure work. More model progress.
Route to the right GPU
Prioritize accelerator families and regions, then fail over when a pool is constrained.
Observe every request
Track queue delay, execution time, cold starts, worker health, releases and spend from one view.
From code to capacity
One clean path to production.
1
Connect source
Choose a model template, GitHub repository or Docker image.
2
Set scaling policy
Define GPU priorities, concurrency, worker floor and ceiling.
3
Call your endpoint
Use a queue API, synchronous route or OpenAI-compatible interface.
deploy.py
from gpurento import serverless
@serverless.endpoint(
gpu=["L40S", "A100-80GB"],
min_workers=0,
max_workers=24,
concurrency=8,
)
def generate(prompt: str):
return model(prompt)✓ Configuration saved to the workspace · provisioning state visible
Included
A serious platform, not a thin wrapper.
Every workload gets the same API, identity, billing and observability layer.
Flex and active worker modes
Queue and load-balancer endpoints
GPU priority and regional routing
Model cache and persistent storage
Request logs and live worker states
Versioned releases and rollback
Webhook delivery and retries
OpenAI-compatible model routes
How does scale-to-zero work?+
When no requests remain and the idle window expires, flex workers shut down. The next request starts a compatible worker and the queue preserves the job.
Can I keep workers warm?+
Yes. Set a minimum worker count for steady traffic or use scheduled capacity for predictable peaks. The cost estimate updates before the policy is saved.
Can I deploy from GitHub?+
The product flow supports GitHub, container registries, curated model templates and a Python-first remote function workflow.
How are failures handled?+
Jobs expose clear queued, running, completed, retried and failed states. Retry policy, timeout, job TTL and webhook behavior remain configurable.
Ready when you are