Production Metrics

vLLM exposes a number of metrics that can be used to monitor the health of the system. These metrics are exposed via the /metrics endpoint on the vLLM OpenAI compatible API server.

The following metrics are exposed:

class Metrics:

    def __init__(self, labelnames: List[str]):
        # Unregister any existing vLLM collectors
        for collector in list(REGISTRY._collector_to_names):
            if hasattr(collector, "_name") and "vllm" in collector._name:
                REGISTRY.unregister(collector)

        # Config Information
        self.info_cache_config = Info(
            name='vllm:cache_config',
            documentation='information of cache_config')

        # System stats
        self.gauge_scheduler_running = Gauge(
            name="vllm:num_requests_running",
            documentation="Number of requests currently running on GPU.",
            labelnames=labelnames)
        self.gauge_scheduler_swapped = Gauge(
            name="vllm:num_requests_swapped",
            documentation="Number of requests swapped to CPU.",
            labelnames=labelnames)
        self.gauge_scheduler_waiting = Gauge(
            name="vllm:num_requests_waiting",
            documentation="Number of requests waiting to be processed.",
            labelnames=labelnames)
        self.gauge_gpu_cache_usage = Gauge(
            name="vllm:gpu_cache_usage_perc",
            documentation="GPU KV-cache usage. 1 means 100 percent usage.",
            labelnames=labelnames)
        self.gauge_cpu_cache_usage = Gauge(
            name="vllm:cpu_cache_usage_perc",
            documentation="CPU KV-cache usage. 1 means 100 percent usage.",
            labelnames=labelnames)

        # Raw stats from last model iteration
        self.counter_prompt_tokens = Counter(
            name="vllm:prompt_tokens_total",
            documentation="Number of prefill tokens processed.",
            labelnames=labelnames)
        self.counter_generation_tokens = Counter(
            name="vllm:generation_tokens_total",
            documentation="Number of generation tokens processed.",
            labelnames=labelnames)
        self.histogram_time_to_first_token = Histogram(
            name="vllm:time_to_first_token_seconds",
            documentation="Histogram of time to first token in seconds.",
            labelnames=labelnames,
            buckets=[
                0.001, 0.005, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.25, 0.5,
                0.75, 1.0, 2.5, 5.0, 7.5, 10.0
            ])
        self.histogram_time_per_output_token = Histogram(
            name="vllm:time_per_output_token_seconds",
            documentation="Histogram of time per output token in seconds.",
            labelnames=labelnames,
            buckets=[
                0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75,
                1.0, 2.5
            ])
        self.histogram_e2e_request_latency = Histogram(
            name="vllm:e2e_request_latency_seconds",
            documentation="Histogram of end to end request latency in seconds.",
            labelnames=labelnames,
            buckets=[1.0, 2.5, 5.0, 10.0, 15.0, 20.0, 30.0, 40.0, 50.0, 60.0])

        # Legacy metrics
        self.gauge_avg_prompt_throughput = Gauge(
            name="vllm:avg_prompt_throughput_toks_per_s",
            documentation="Average prefill throughput in tokens/s.",
            labelnames=labelnames,
        )
        self.gauge_avg_generation_throughput = Gauge(
            name="vllm:avg_generation_throughput_toks_per_s",
            documentation="Average generation throughput in tokens/s.",
            labelnames=labelnames,
        )