The first token tells me admission and initial prefill worked for those requests. It does not prove decode stayed healthy for the next 2,000 tokens. Nor does a healthy backend average prove a user saw the chunks promptly. I would take a handful of affected request IDs and build the timeline of each visible output event, not just request start and finish.

At the model worker, record when a decode step finishes and when the server publishes the corresponding event. At the gateway, record receipt, any batching or transformation, and socket write completion. At the edge and client, record event receipt and when the UI consumes it. Use a request ID and monotonically increasing event number through the path. Clock skew makes cross machine timestamps tricky, so use local monotonic gaps within a hop and enough correlation to locate the first growing gap. This can be done without recording the user's tokens in a general purpose trace.

Suppose the worker also has a 12 second gap between output events. That is an inference path problem. Look at queueing during the run, decode scheduling, KV pressure, preemption or recomputation, memory transfers, and whether one tenant's long outputs are crowding others. Slice by model pool, context length, output position, active sequences, and KV occupancy. vLLM's metrics distinguish inter event latency, decode time, queue time, and preemptions. Their exact definitions matter. A per request average output rate can conceal one long freeze surrounded by many fast tokens.

If the worker publishes regularly and the gateway receives regularly, the next boundary is transport. Did the gateway flush events promptly, or buffer them while applying formatting, moderation, or compression? Did a proxy impose an idle timeout or coalesce small writes? Did flow control stop the server from writing because the downstream socket could not drain? A successful write into a local buffer is not proof that the browser rendered the event. Compare byte counts and buffer age at each hop, and check whether stalled requests share a proxy, client version, or region. The server sent events standard defines event framing and reconnection behavior, but it does not guarantee every proxy or application flushes each event immediately.

There is a third case: the browser receives events on time but the UI blocks on parsing, rendering, or a slow consumer loop. That is a real stall to the user, even while every server metric stays green. Test with a minimal client that reads without rendering and another that deliberately consumes slowly. If slowing the client causes upstream backpressure, decide whether to pause generation, bound the buffer, or end the stream cleanly. Generating unbounded text into a buffer for an absent reader wastes GPU work and memory.

Long outputs make the diagnosis more interesting. The correlation might reflect growing KV memory or contention later in decode. It might be a gateway buffer threshold reached after several megabytes. Or the reported “stall” may simply be the interval when the model is emitting non visible events or the client has stopped reading. I would plot gap size by output position and compare the same length requests across affected and unaffected pools. Correlation with length by itself cannot identify the failing hop.

What if the backend says it emitted every token evenly but the client has one 20 second gap? Before blaming the network, verify what “emitted” means in that metric. It could mean decoded tokens before detokenization, chunks queued in the application, or completed socket writes. Add the missing boundary timestamp and reproduce with a controlled slow reader. If a generator is canceled after the client disconnects, verify the worker actually stops and releases its KV allocation. A closed browser tab should not keep a hidden long generation alive.

I would fix the first hop at which the gap appears, then load test with realistic long outputs and slow clients. The release measure is the distribution of user visible inter event gaps and completed streams, alongside backend decode gaps and wasted tokens. An average tokens per second graph can be healthy while the product repeatedly freezes in the middle of a response.