Assessment

Every diagnose run ends with an assessment — a concise "Likely bottleneck" summary that appears before the detailed findings. Where the findings list individual symptoms, the assessment interprets the combination of them into a single most-likely root cause, so you can start triage without reading every rule.

The assessment never replaces or hides the findings — it sits on top of them.

Example

Likely bottleneck: KV cache saturation (high confidence)
  Requests are likely waiting because the server has limited KV cache
  headroom, often caused by high concurrency or long-context requests.

The same summary appears as a top-level assessment object in --output json (see JSON Output).

Categories

The assessment classifies the run into one of:

Category	Meaning
Queue saturation	Requests arrive faster than they can be served; the queue grows
KV cache saturation	Requests wait on limited KV cache headroom
Long prefill	High TTFT with no queue — long input prompts dominate prefill
Decode / TPOT	Per-token generation is the bottleneck, not queueing
Replica imbalance	Load is unevenly distributed across replicas
Error or failure	The server is returning errors or aborting requests
Idle	No active traffic; throughput/latency warnings are suppressed
No clear bottleneck	Evidence is weak or conflicting

How to read it

The assessment summarizes the findings and measurements from the current run. Its evidence contains the observed values supporting the diagnosis, and its confidence indicates how strongly those values point to the reported bottleneck.

When evidence is weak or conflicting, vLLM Doctor reports No clear bottleneck or low confidence rather than guessing.

Sequence-length evidence is optional

Distinguishing long-prefill from decode-heavy workloads is sharper when vLLM's prompt/generation token histograms (vllm:request_prompt_tokens_bucket, vllm:request_generation_tokens_bucket) are available. When they are not exposed, the assessment still works — it simply relies on latency and queue signals and does not use prompt/output length evidence.