GPU inference platform
Everybody wants to ship a model, nobody wants to run one
Serving a language model in production is a different job from choosing one. This is the layer that makes the second job someone else's problem: models go up, scale with demand, and report on themselves.
- Role
- Platform and inference infrastructure
- Timeline
- 2026, ongoing
- Runtime
- vLLM on GPU nodes
A model that works in a notebook is not a service. It needs a GPU node that is the right size, a runtime that batches requests properly, somewhere for the weights to live so a restart is not a download, and autoscaling that reacts to real traffic rather than to CPU.
Product teams end up either overprovisioning a GPU that sits idle most of the day, or hand rolling a serving stack that only one person understands. Neither survives contact with a second model.
- Served
- 0.0/s
- Queue
- 0
- Replicas
- 2
- Latency
- 0 ms
- GPU busy
- 0%
- Two replicas ready. Press play.
A model of the system, not a capture from the cluster. Turn batching off, or weights off the shared volume, and watch where the time actually goes.
One platform, many models
vLLM does the serving, so continuous batching and paged attention come for free rather than being reinvented. Kubernetes does the scheduling, which means GPU nodes are a pool rather than a pet, and a model is a workload like any other.
On top of that sits the part teams actually touch: deploy a model, scale it, see its metrics, and benchmark one against another under the same load before committing to it. The comparison is the point. Choosing a model on published numbers rather than your own traffic is how you end up paying for capacity you do not need.