Compute / Inference

Deploy versioned inference endpoints on selected GPU capacity.

Define the model reference, region, accelerator, replica range, target concurrency, network boundary, rollout policy, metering, and maximum exposure before an authenticated deployment.

This public experience documents the deployment contract. It does not claim a live managed endpoint, model compatibility, latency, throughput, or availability without current measured evidence.

Find inference GPUs Reserve serving capacity

Immutable revisions

Pin model, runtime, image, configuration, accelerator, and environment into a revision that can be promoted or rolled back.

Measured behavior

Track queue time, time to first token, throughput, errors, saturation, replica state, and cost without converting modeled values into measured claims.

Bounded autoscaling

Minimum and maximum replicas, target concurrency, warm capacity, scale delays, budget, and provider quota form one policy.

Private by default

Authentication, private networking, secrets, model access, request logging, retention, and egress controls are deployment requirements.

Inspect	Required decision
Model	Artifact, license, format, tokenizer, precision, context, and runtime compatibility
Capacity	Accelerator, VRAM, replica range, concurrency, region, quota, and fallback
Exposure	Private/public access, identity, rate limit, maximum spend, and data policy
Release	Health gate, traffic shift, rollback, evidence retention, and cleanup

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