Do not send inference requests just because a local LLM server’s port is open. Hold them behind a documented readiness check, keep the waiting queue bounded, and enforce one deadline across startup, queueing, and generation. Once the model is ready, dispatch only within the server’s available concurrency; discard work that has expired or been cancelled.
Why an open port is not enough
A process can accept network connections before its model has finished loading. Treating a successful TCP connection as readiness can send inference work too early. Use a health or readiness signal that distinguishes loading from ready, and follow the behavior documented for the server version you actually run.
llama.cpp readiness
The llama.cpp server README documents GET /health: it returns HTTP 503 while the model is loading and HTTP 200 when the model is ready. A client can use that response to decide when requests may be released. The documentation is on the project’s current master branch, so verify that the installed build has the same behavior: llama.cpp server README.
Build a bounded waiting queue
Give the application queue an explicit maximum depth. When it is full, reject or surface an overload result rather than accepting an unlimited backlog that can grow stale or consume resources. The exact limit and overload response are application decisions; server controls vary. vLLM’s latest serving CLI documentation describes a request limit that bounds its otherwise unbounded request queue, but confirm the option and semantics for your release: vLLM serving CLI arguments.
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Track each queued item’s arrival time, deadline, and cancellation state. This lets the dispatcher tell useful work from requests that have already outlived the caller’s need.
Use one end-to-end deadline
Set a deadline for the whole operation, not a fresh timeout for each stage. Time spent waiting for the model to load and waiting for an execution slot counts against the same budget as inference. When readiness arrives, calculate the remaining time from the original deadline; do not reset the clock and grant the request a second full timeout.
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There is no universal startup timeout or retry interval established by the server documentation cited here. Choose a bounded retry policy and a total deadline based on measurements from the actual hardware, model, request type, and server version. Avoid automatic retries that can silently duplicate work if the original request may already have started.
Release work only when both readiness and capacity allow
Readiness means the model can serve; it does not mean the server has unlimited execution capacity. llama.cpp documents configurable parallel slots, with each slot holding one conversation. Gate dispatch on available slots as well as the ready signal, and check the installed release’s supported options before configuring parallelism: llama.cpp serving guide.
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Keep requests waiting when capacity is temporarily occupied, subject to their original deadlines and the queue limit. Do not assume all servers use the same ordering, expose capacity to a proxy, or offer the same fairness guarantees.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Propagate cancellation and discard stale work
Before dispatch, remove any request whose caller has cancelled or whose deadline has passed. This avoids spending inference capacity on work that no longer has a recipient.
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If a request has already been sent to the server, use a supported abort mechanism where one exists. vLLM’s online serving documentation describes /abort_requests, including optional targeting by request IDs; verify the endpoint’s availability and semantics in the deployed release: vLLM online serving. Queued cancellation in your application and aborting already-dispatched inference are separate operations.
Account for memory pressure and version differences
Model loading may be delayed when available memory is insufficient. An older Ollama documentation mirror describes requests being queued in a situation where other loaded models leave insufficient memory for the requested model. Because that result is from an older mirror, treat it as a possible behavior to verify, not as proof of current defaults or settings: Ollama FAQ.
Across servers, check readiness semantics, queue bounds, concurrency, cancellation, and timeout behavior against the exact installed release and deployment. The cited documentation gives examples of these features, not a complete like-for-like comparison; it does not establish universal FIFO ordering or identical cancellation guarantees.
Operational signals worth recording
Monitor queue depth, oldest request age, startup duration, overload rejections, and cancellations. These are useful application-level signals; the cited documentation does not establish that every server exposes them by default. Together they help distinguish slow model startup from insufficient execution capacity or a queue limit that is too permissive.
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