vllm-htop 0.2.0

htop-style terminal monitor for vLLM inference servers

vllm-htop

htop for vLLM inference servers — point it at one or more /metrics endpoints and get the right numbers, the right way, right now.

Zero dependencies. Single file. Python 3.8+.

vLLM DP Monitor  │  4/4 up  │  2026-05-18 14:23:01  (interval=2.0s)
──────────────────────────────────────────────────────────────────────────────────
 DP  Status   Run  Wait  Swap   KV%      in tok/s  out tok/s   TTFT-P95  TPOT-P95
──────────────────────────────────────────────────────────────────────────────────
 0   OK       12     0     0    55.0%       49793      16597       410ms     37.0ms
 1   OK       11     0     0    58.0%       47841      15947       415ms     38.0ms
 2   OK       18     6     0    91.0%       69738      23246       820ms     52.0ms
 3   OK       12     0     0    57.0%       48100      16100       420ms     38.0ms
──────────────────────────────────────────────────────────────────────────────────
 ALL          53     6     0   max91.0%      215472      71890       512ms     41.0ms

▸ Imbalance check  (across 4 replicas)
  Running req     :    11  →  18    (Δ=7)             ⚠ load-balancer skew?
  KV cache        :  55.0% → 91.0%  (Δ=36.0pp)        ⚠ uneven KV pressure
  TTFT P95        :   410ms → 820ms (2.00×)           ⚠ slow replica
  TPOT P95        :  37.0ms → 52.0ms (1.41×)

▸ Cumulative  (life = vLLM counters · sess = peaks observed since monitor uptime 12m34s)
──────────────────────────────────────────────────────────────────────────────────
 DP   life-Prompt  life-Output  life-Reqs   peak-Run  peak-Wait  peak-KV%   peak in/out tok/s
──────────────────────────────────────────────────────────────────────────────────
 0        12.3M         3.4M        10.2K       19         4     71.3%   52.1K/17.4K
 1        11.9M         3.3M         9.9K       17         2     67.8%   50.3K/16.8K
 2        13.1M         3.7M        11.2K       28        12     91.0%   72.4K/24.1K
 3        12.1M         3.4M        10.1K       18         3     68.5%   51.2K/17.1K
──────────────────────────────────────────────────────────────────────────────────
 ALL      49.4M        13.8M       41.4K

Why?

vLLM exports a rich Prometheus /metrics endpoint with everything you need to understand serving performance — TTFT/TPOT/E2E histograms, KV cache usage, queue depth, swap counts. But...

• ...running production Prometheus + Grafana is overkill when you just SSH'd in and want to know if a server is healthy right now
• ...curl /metrics | grep can't compute windowed percentiles or rates
• ...when you run Data Parallel replicas, you really want side-by-side comparison and imbalance detection, which the default vLLM Grafana dashboard doesn't surface at all

vllm-htop is the thing you reach for between Grafana (always-on, persistent) and curl (one-off, raw). It complements both — not a replacement.

Install

The fastest way — no install needed (recommended):

uvx vllm-htop --url http://localhost:8000

With pip:

pip install vllm-htop
vllm-htop --url http://localhost:8000

Or just grab the single file and run it (no dependencies needed beyond Python 3.8+):

curl -O https://raw.githubusercontent.com/eyuansu62/vllm-htop/main/vllm_htop.py
python vllm_htop.py --url http://localhost:8000

Usage

Single instance — detail view

vllm-htop --url http://localhost:8000

Shows P50/P95/P99 across TTFT/TPOT/E2E/Queue, current saturation gauges, and lifetime cumulative.

DP / multiple replicas — comparison table

# Space-separated
vllm-htop --url http://h1:8000 http://h2:8000 http://h3:8000 http://h4:8000

# Comma-separated
vllm-htop --url http://h1:8000,http://h2:8000,http://h3:8000,http://h4:8000

# Shell brace expansion (most concise)
vllm-htop --url http://localhost:{8000,8001,8002,8003}

Automatically switches to compact per-replica rows + aggregate + imbalance check.

Auto-discovery — one machine, many DP replicas

If you don't pass --url, vllm-htop scans localhost:8000-8015 for vLLM-shaped /metrics endpoints and attaches to whatever it finds. So when you have multiple vllm serve processes on the same host (one per port), monitoring all of them is just:

vllm-htop

It narrates the discovery only when interesting (≥2 endpoints found, or --auto was explicit); the single-instance case stays quiet.

# Force discovery (fails loudly if nothing's found — useful in scripts)
vllm-htop --auto

# Wider range, different host
vllm-htop --auto --host 10.0.0.7 --port-range 9000-9031

Discovery does a parallel TCP probe over the range, then HTTP-probes only the open ports for the vllm: metric-name prefix, so it's fast (typically <100ms on a localhost scan) even on wide ranges. Non-vLLM services on the same ports are filtered out, not confused for replicas.

If discovery turns up nothing and you didn't pass --auto, the tool falls back to http://<host>:8000 and surfaces the real fetch error there — more useful than a generic "no endpoints found".

Cost estimation (optional)

Two independent pricing models, either or both can be on:

Token-based — explicit prices in $/1M tokens (OpenAI-style convention):

vllm-htop --cost-in 0.50 --cost-out 1.50

Compute-based — auto-detected from nvidia-smi, with a built-in GPU price-hint table:

# Just run it. If `nvidia-smi` is on PATH, vllm-htop reads the GPU model and
# count, looks up a community-market reference rate, and shows compute burn.
vllm-htop

# Or override the rate / count explicitly:
vllm-htop --gpu-cost-hour 2.99 --num-gpus 8

# Skip the auto-detect entirely:
vllm-htop --no-gpu-detect

The built-in hints cover:

• Blackwell datacenter: B200, B100, GB200
• Blackwell workstation/consumer: RTX PRO 6000, RTX 5090, RTX 5080
• Hopper: H100, H100 NVL, H200
• Ampere: A100 (40/80GB), A40, A30, A10, A10G, RTX A6000/A5000/A4000, RTX 3090
• Ada Lovelace: L40S, L40, L4, RTX 6000 Ada, RTX 4090, RTX 4080
• Older datacenter: V100, T4

Prices are anchored to RunPod Secure tier published rates as of 2026-05 — this is what OpenRouter-class token-API providers (Lambda, Hyperbolic, DeepInfra, …) typically pay for their compute, so it's the most representative "GPU rental cost" for someone running their own vLLM stack. Cross-provider variance:

• AWS / GCP on-demand: 3-5× higher
• Lambda Labs: within ±10%
• RunPod Community: 20-40% lower
• vast.ai community: 30-50% lower (high variance)

Treat the numbers as a ballpark (±30%) and override via --gpu-cost-hour for anything serious.

Both at once — also surfaces a Margin row (token revenue ÷ compute cost):

vllm-htop --cost-in 0.50 --cost-out 1.50 --gpu-cost-hour 2.99 --num-gpus 8

Example output:

▸ Cost  (estimated · sum across 3 replicas)
  Token-based  ($0.5/M in, $1.5/M out)
    Lifetime     :      $165.17  ($75.08 in + $90.09 out)
    This session :        $0.13  (over 2m11s)
    Current rate :        $3.86/min  ($231.55/hour at current throughput)
  Compute-based  (NVIDIA H100 80GB HBM3 × 8 @ $2.99/h — auto-detected, estimate)
    Burn rate    :       $23.92/hour  (paid whether busy or idle)
    This session :         $0.87  (over 2m11s)
  Margin (token revenue ÷ compute cost)
    At current load :       9.68×  ($231.55/h revenue vs $23.92/h compute)

The Cost section is hidden when no pricing is configured (no --cost-* flags and GPU auto-detect found nothing).

Flags

Flag	Default	What it does
--url URL [URL ...]	(auto-discovery)	Explicit vLLM base URLs. Overrides auto-discovery
--auto	(implicit default)	Force discovery, fail loudly if nothing found. Without --url, discovery already runs implicitly
--host HOST	localhost	Hostname for discovery and the fallback URL
--port-range LO-HI	8000-8015	Port range for discovery (e.g. 8000-8015, 8000:8015)
--interval N	2.0	Refresh interval in seconds
--timeout N	4.0	Per-endpoint fetch timeout
--once	off	Print one snapshot and exit (good for cron / CI smoke tests)
--table	auto	Force compact table view
--cost-in PRICE	off	USD per 1M input (prompt) tokens — enables token-based Cost section
--cost-out PRICE	off	USD per 1M output (generation) tokens
--gpu-cost-hour PRICE	auto	USD per GPU-hour. Defaults to a built-in hint based on nvidia-smi detection
--num-gpus N	auto	GPU count. Defaults to nvidia-smi count
--no-gpu-detect	off	Skip nvidia-smi auto-detection entirely
--currency SYM	$	Currency symbol shown in the Cost section
--detail	auto	Force per-instance detail view

What it shows

Throughput (windowed)

Token and request rates computed from the delta between the last two polls — reflects recent behavior, not lifetime average.

Latency (windowed percentiles)

P50/P95/P99 for TTFT, TPOT, E2E, queue time. Percentiles come from histogram bucket deltas between polls — equivalent to Prometheus' histogram_quantile(0.95, rate(..._bucket[Δ])).

Saturation (current gauges)

Running / waiting / swapped requests, plus KV cache usage with a colored bar (green < 65%, yellow < 85%, red ≥ 85%).

Imbalance check (DP only, ≥2 replicas)

Check	Threshold	Means
Running req	ratio > 1.5× and Δ > 3	⚠ load-balancer skew / sticky session
KV cache	Δ > 15 percentage points	⚠ uneven KV pressure (prefix-cache asymmetry?)
TTFT P95	max/min > 1.5×	⚠ slow replica (GPU thermal, NCCL, contention)
TPOT P95	max/min > 1.5×	⚠ slow decode

Cumulative

Two clearly-labelled sources:

• life — read directly from vLLM *_total counters: prompt tokens, output tokens, successful requests since vLLM started
• sess — peaks observed by the monitor since it started watching: peak running / waiting / KV% / tokens/s

swap-seen is sticky within a session: if swapping fires once, it stays red as a warning even after it recovers.

Design notes

• Aggregate percentiles across DP are computed by merging histogram buckets — that's the only mathematically correct way to combine percentiles. Averaging per-replica P95s is wrong.
• DOWN replicas are isolated — they don't break the table, aggregate, or imbalance check. The header shows 3/4 up and the offending row stays visible with its error.
• STALE status: last fetch failed but we have an older snapshot, useful for transient network blips.
• Parallel polling via ThreadPoolExecutor — refresh time stays ≈ slowest single fetch regardless of replica count.
• Metric-name matching is substring-based (time_to_first_token, cache_usage_perc) so the tool tolerates vLLM version drift between vllm:gpu_cache_usage_perc and vllm:kv_cache_usage_perc.

Limitations

• vLLM internal DP (vllm serve --data-parallel-size N where multiple ranks share one /metrics endpoint and label samples with engine="0", engine="1", etc.): currently all engines' samples are summed into one. For per-rank visibility, run a separate vllm-htop per endpoint, or open an issue for --split-by-label support.
• Peaks are in-memory only — when the script exits, session peaks are lost. For long-term persistence, use Prometheus.
• No alerting — this is a viewer, not a notifier. For real alerting see Andrey Krisanov's vLLM Prometheus rules as a starting point.

Acknowledgments

The vLLM project for exposing rich metrics by default, and for shipping a reference Grafana dashboard that informed the choice of which metrics matter most.

License

MIT