GPU production inference
Pulsar with vLLM
Use vLLM as a private OpenAI-compatible model runner with backend-owned capacity control and operator-visible diagnostics.
Key considerations
Production path
OpenAI-compatible chat inference on approved GPU infrastructure.
Shared capacity gate
Browser and API streaming use the same Valkey-backed admission control.
Visible diagnostics
Operators can inspect effective capacity, source, and active slots.
Architecture at a glance
Use vLLM when inference is a production service
vLLM fits deployments that need a dedicated GPU-backed model endpoint, higher throughput, longer context, and OpenAI-compatible request handling.
Capacity remains backend-owned
Pulsar resolves an effective global stream cap at startup from an explicit override or current vLLM model metadata. The value is cached rather than polled on each request, and discovery failure falls back to a conservative capacity.
One admission path for browser and API traffic
Interactive chat and OpenAI-compatible streaming share the same Valkey-backed slot gate. Valkey uses the Redis-compatible protocol, so existing admission clients remain compatible.
Operators retain the evidence
The administration surface reports runner reachability and capacity diagnostics, while the deployment review determines model choice, GPU sizing, context, and failure behavior.
Private deployment consultation
Review Pulsar against your environment.
Bring the infrastructure, security boundaries, model runners, and use cases. The Pulsar team will map the appropriate deployment path.