Local Model Serving
Our infrastructure layer can expose private or open-weight models as production services inside controlled compute environments.
More than loading weights
Serving a local model requires more than loading weights onto a GPU. The runtime must manage:
- Model execution
- Memory management
- Batching
- Concurrency
- API access
- Monitoring
- Versioning
Serving path
- Request
- Authenticated API
- Queue / scheduler
- Model runtime
- GPU memory + execution
- Response
- Metrics / logs / trace
The API boundary allows the rest of the platform to treat local inference as a governed service rather than requiring every application to manage model processes directly.
Concurrency and batching
The serving layer balances latency against utilization. Interactive traffic can require prompt scheduling and bounded queue depth. High-volume workloads may benefit from batching or asynchronous execution.
Concurrency also changes memory requirements. Context state and generation caches can consume substantial VRAM even when all requests share the same model weights. Production capacity is therefore measured under expected request patterns, not only through single-request benchmarks.
Versioning
A local model endpoint represents a specific deployed artifact: model version, precision, runtime configuration, and potentially an adapter or specialization layer. Changing any of those variables can change behavior.
We treat upgrades as production changes that require evaluation and rollback capability rather than silently replacing weights behind a stable endpoint.
Operating burden
Local serving provides greater control over privacy, model version, latency, and unit economics. It also transfers responsibility for uptime, capacity, runtime compatibility, monitoring, and hardware utilization to us.
We use local model serving where owning that infrastructure creates a measurable advantage over consuming an external endpoint.
Admission control
A local endpoint should not accept unlimited work simply because requests can reach it. Queue depth, concurrency limits, context size, and workload priority can be bounded so temporary traffic spikes do not force the runtime into repeated out-of-memory failures or unbounded latency. Admission control can reject, queue, or reroute work before the accelerator is overloaded. This is especially important when the same hardware serves both recurring background jobs and latency-sensitive requests.
A runtime can remain technically online while memory fragmentation, queue saturation, or a damaged model load makes it operationally unhealthy.
Serving therefore needs health semantics that distinguish a process being alive from a model endpoint being usable. Local serving combines model execution with ordinary production-service disciplines rather than treating successful model loading as sufficient evidence of readiness.
Scope
This page is the serving engineering: runtime, memory, batching, concurrency, versioning, admission control and health — what it takes to keep a model answering reliably under real load.
Which models are available to serve is Open-Weight Models. Whether a workload belongs on this infrastructure at all is Private / Local Models.