LMDeploy request prefill time histogram
niche-lmdeploy-turbomind · histogram
Fields
id
metric-exposures/w3m-niche-lmdeploy-turbomind-lmdeploy-request-prefill-time-secondsMetric name
lmdeploy:request_prefill_time_seconds
Metric type
histogram
Unit
seconds
Exposure
prometheus
Endpoint
:23333/metrics
Histogram buckets
0.30.50.811.522.551015203040506012024048096019207680
Labels
model_nameengine
Enabled by default
yes
Phase scoped
yes
notes
InternLM/lmdeploy main, PrometheusStatLogger in lmdeploy/metrics/loggers.py, read 2026-10-04. On by default and requires NO flag: the endpoint is simply available at the API server port (23333 by default). Label keys are model_name and engine on every metric, where engine carries the dp_rank. Bucket construction reuses vLLM's build_buckets / build_1_2_5_buckets idiom. Shared request_latency ladder. The whole per-request phase set is observed in record_finish(); the prefill/decode split is what makes this set phase-scoped even though TTFT/TPOT are observed per iteration.
Notes
InternLM/lmdeploy main, PrometheusStatLogger in lmdeploy/metrics/loggers.py, read 2026-10-04. On by default and requires NO flag: the endpoint is simply available at the API server port (23333 by default). Label keys are model_name and engine on every metric, where engine carries the dp_rank. Bucket construction reuses vLLM's build_buckets / build_1_2_5_buckets idiom. Shared request_latency ladder. The whole per-request phase set is observed in record_finish(); the prefill/decode split is what makes this set phase-scoped even though TTFT/TPOT are observed per iteration.