Frank
c42780f9f3
- shared/prometheus/rules/llm-fleet.yml: the llamacpp reuse recording rule divided cached by prompt, but llama.cpp's prompt_tokens_total EXCLUDES cached tokens (its own HELP line), so the ratio was unbounded — measured 77 on a fully-warm frank-large, meaning LlmPrefixCacheCollapse could never fire on that side. Now cached / (cached + prompt), with the traffic floor counting both terms too; the header documents the unit trap. Also fixed the LlmEndpointDown description: frank-large's CIRU llama.cpp build DOES export the llamacpp:* family (verified live), so the alert covers all three endpoints. - shared/grafana/provisioning/dashboards/llm-fleet.json: re-cut for two namespaces. Every panel reads both (the endpoint picker selects); the KV-pool-fill panel retired 2026-09-16 returned with vllm:kv_cache_usage_perc; the latency p95 panel returned with the vLLM histograms (TTFT/ITL/e2e); the alerts panel is scoped to the fleet's Llm.* rules; the stale halogen-era description is gone. All 21 panel expressions validated against the live endpoints. - runbook + log + Observability: prefix caching measured, not inferred — reuse happens at 1,648-token block granularity (attention block size padded to the mamba page size); an identical 13,021-token prompt went 2.30 s cold to 0.68 s warm with 9,888 tokens (76 %) reused, while a 2,521-token prompt registered zero hits. Rule counts corrected to 6 alerts + 3 recording rules. ./tests/run.sh: 325 pass.
315 lines
11 KiB
JSON
315 lines
11 KiB
JSON
{
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"uid": "llm-fleet",
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"title": "LLM fleet \u2014 capacity and reuse",
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"description": "The signals that explain the LLM endpoints under load. The fleet runs two runtimes and the panels read both: vllm:* for frank-medium (Dankserver, vllm-radiance TP=2 across both R9700s since 2026-09-20) and llamacpp:* for frank-small (Dankclaw, RTX 3090) and frank-large (Dankclaw, gfx1151 iGPU \u2014 the CIRU llama.cpp build does export metrics, verified 2026-09-20). The endpoint picker selects between them. See serverAppConfigs-docs/LLM/frank-medium-radiance-mxfp4.md for the numbers these panels are read against, and serverAppConfigs-docs/Infrastructure/Observability.md for the namespace split.",
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"tags": [
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"llm",
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"frank-medium",
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"capacity"
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],
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"timezone": "browser",
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"schemaVersion": 39,
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"version": 1,
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"refresh": "30s",
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"time": {
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"from": "now-6h",
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"to": "now"
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},
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"templating": {
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"list": [
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{
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"name": "job",
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"label": "endpoint",
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"type": "query",
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"datasource": {
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"type": "prometheus",
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"uid": "prometheus"
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},
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"query": {
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"query": "label_values({__name__=~\"vllm:kv_cache_usage_perc|llamacpp:.*\"}, job)",
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"refId": "A"
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},
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"includeAll": true,
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"multi": true,
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"current": {
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"text": "All",
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"value": "$__all"
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}
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}
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]
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},
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"panels": [
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{
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"id": 1,
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"type": "timeseries",
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"title": "Prefix cache reuse rate",
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"description": "rate(hits)/rate(queries) in tokens. On the llama.cpp side the prompt counter EXCLUDES cached tokens (its own HELP line), so that side divides by cached + prompt. High means pinned conversations are served from cache: measured 2026-09-20, an identical 13,021-token prompt went 2.30 s cold to 0.68 s warm with 9,888 tokens (76 %) reused. Reuse on frank-medium happens at 1,648-token block granularity (the attention block size is padded to the mamba page size), so shared prefixes shorter than two blocks re-prefill.",
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"gridPos": {
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"h": 8,
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"w": 12,
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"x": 0,
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"y": 0
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},
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"datasource": {
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"type": "prometheus",
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"uid": "prometheus"
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},
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"fieldConfig": {
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"defaults": {
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"unit": "percentunit",
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"min": 0,
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"max": 1,
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"custom": {
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"fillOpacity": 10,
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"lineWidth": 1
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}
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},
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"overrides": []
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},
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"targets": [
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{
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"expr": "rate(llamacpp:prompt_tokens_cached_total{job=~\"$job\"}[5m]) / clamp_min(rate(llamacpp:prompt_tokens_cached_total{job=~\"$job\"}[5m]) + rate(llamacpp:prompt_tokens_total{job=~\"$job\"}[5m]), 0.001)",
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"legendFormat": "{{job}} reuse (llama.cpp)",
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"refId": "A"
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},
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{
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"expr": "job:llm_vllm_prefix_cache_reuse:rate5m{job=~\"$job\"}",
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"legendFormat": "{{job}} reuse (vLLM)",
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"refId": "B"
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}
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]
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},
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{
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"id": 2,
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"type": "timeseries",
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"title": "Speculative decoding (acceptance)",
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"description": "The one failure no client can see: the drafter silently stops and the endpoint keeps answering at autoregressive speed. llama.cpp: accepted tokens per draft step. vLLM: mean accepted length (1 + accepted/drafts) from the recording rule job:llm_spec_accept_length:rate5m \u2014 measured 3.0-4.3 at boot 2026-09-20 with the DFlash2 drafter.",
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"gridPos": {
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"h": 8,
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"w": 12,
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"x": 12,
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"y": 0
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},
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"datasource": {
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"type": "prometheus",
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"uid": "prometheus"
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},
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"targets": [
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{
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"expr": "rate(llamacpp:spec_decode_num_accepted_tokens_total{job=~\"$job\"}[5m]) / clamp_min(rate(llamacpp:spec_decode_num_drafts_total{job=~\"$job\"}[5m]), 0.001)",
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"legendFormat": "{{job}} accepted per draft step",
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"refId": "A"
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},
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{
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"expr": "job:llm_spec_accept_length:rate5m{job=~\"$job\"}",
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"legendFormat": "{{job}} mean accepted length (vLLM)",
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"refId": "B"
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},
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{
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"expr": "rate(llamacpp:spec_decode_num_draft_tokens_total{job=~\"$job\"}[5m])",
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"legendFormat": "{{job}} draft tokens/s",
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"refId": "C"
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}
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]
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},
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{
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"id": 3,
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"type": "timeseries",
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"title": "Requests waiting (queue depth)",
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"description": "llama.cpp: anything above zero means the engine is serialising (with --parallel 1 a second concurrent request always shows up here). vLLM: the scheduler's own waiting/running gauges against MAXSEQS=8.",
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"gridPos": {
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"h": 8,
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"w": 12,
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"x": 0,
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"y": 8
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},
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"datasource": {
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"type": "prometheus",
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"uid": "prometheus"
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},
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"targets": [
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{
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"expr": "llamacpp:requests_deferred{job=~\"$job\"}",
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"legendFormat": "{{job}} deferred",
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"refId": "A"
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},
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{
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"expr": "llamacpp:requests_processing{job=~\"$job\"}",
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"legendFormat": "{{job}} processing",
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"refId": "B"
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},
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{
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"expr": "vllm:num_requests_waiting{job=~\"$job\"}",
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"legendFormat": "{{job}} waiting (vLLM)",
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"refId": "C"
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},
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{
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"expr": "vllm:num_requests_running{job=~\"$job\"}",
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"legendFormat": "{{job}} running (vLLM)",
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"refId": "D"
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}
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]
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},
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{
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"id": 4,
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"type": "timeseries",
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"title": "KV pool fill (vLLM)",
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"description": "The gauge that was retired 2026-09-16 for want of a llama.cpp equivalent and came back when frank-medium moved to vLLM: vllm:kv_cache_usage_perc (the pool is auto-sized \u2014 943,581 tokens at boot 2026-09-20). Sustained high fill with a collapsing reuse panel is the thrash; preemptions are the visible symptom of it. The llama.cpp side has no fill gauge: its capacity wall is panel 3.",
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"gridPos": {
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"h": 8,
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"w": 12,
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"x": 12,
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"y": 8
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},
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"datasource": {
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"type": "prometheus",
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"uid": "prometheus"
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},
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"targets": [
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{
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"expr": "vllm:kv_cache_usage_perc{job=~\"$job\"}",
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"legendFormat": "{{job}} KV fill %",
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"refId": "A"
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},
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{
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"expr": "rate(vllm:num_preemptions_total{job=~\"$job\"}[5m])",
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"legendFormat": "{{job}} preemptions/s",
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"refId": "B"
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}
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]
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},
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{
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"id": 5,
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"type": "timeseries",
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"title": "Decode and prefill throughput",
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"description": "llama.cpp: llamacpp:predicted_tokens_seconds is decode, llamacpp:prompt_tokens_seconds is prompt. vLLM: the token counters, rate'd. The series carry the job label of their scrape, so the endpoint picker selects between the runtimes.",
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"gridPos": {
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"h": 8,
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"w": 12,
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"x": 0,
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"y": 16
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},
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"datasource": {
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"type": "prometheus",
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"uid": "prometheus"
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},
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"targets": [
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{
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"expr": "llamacpp:predicted_tokens_seconds{job=~\"$job\"}",
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"legendFormat": "{{job}} decode tok/s (llama.cpp)",
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"refId": "A"
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},
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{
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"expr": "llamacpp:prompt_tokens_seconds{job=~\"$job\"}",
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"legendFormat": "{{job}} prefill tok/s (llama.cpp)",
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"refId": "B"
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},
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{
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"expr": "rate(vllm:generation_tokens_total{job=~\"$job\"}[5m])",
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"legendFormat": "{{job}} decode tok/s (vLLM)",
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"refId": "C"
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},
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{
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"expr": "rate(vllm:prompt_tokens_total{job=~\"$job\"}[5m])",
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"legendFormat": "{{job}} prefill tok/s (vLLM)",
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"refId": "D"
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}
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]
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},
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{
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"id": 6,
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"type": "timeseries",
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"title": "Decode steps and batch size",
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"description": "llama.cpp-only signal \u2014 vLLM does not publish per-step counters. Steps per second and the busy-slot count per step (the effective batch).",
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"gridPos": {
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"h": 8,
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"w": 12,
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"x": 12,
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"y": 16
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},
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"datasource": {
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"type": "prometheus",
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"uid": "prometheus"
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},
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"targets": [
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{
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"expr": "rate(llamacpp:n_decode_total{job=~\"$job\"}[5m])",
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"legendFormat": "{{job}} decode steps/s",
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"refId": "A"
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},
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{
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"expr": "llamacpp:n_busy_slots_per_decode{job=~\"$job\"}",
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"legendFormat": "{{job}} sequences per decode step",
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"refId": "B"
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}
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]
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},
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{
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"id": 7,
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"type": "timeseries",
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"title": "Firing fleet alerts",
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"description": "The LLM-fleet rules from shared/prometheus/rules/llm-fleet.yml. A firing alert is delivered to Signal: Prometheus -> Alertmanager (dankclaw quadlet) -> the host-side signal-relay user unit -> signal-cli, since 2026-09-17.",
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"gridPos": {
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"h": 8,
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"w": 12,
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"x": 0,
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"y": 24
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},
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"datasource": {
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"type": "prometheus",
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"uid": "prometheus"
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},
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"targets": [
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{
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"expr": "ALERTS{alertname=~\"Llm.*\", alertstate=\"firing\"}",
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"legendFormat": "{{alertname}} {{job}}",
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"refId": "A"
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}
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]
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},
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{
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"id": 8,
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"type": "timeseries",
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"title": "Request latency p95 (vLLM)",
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"description": "vLLM-only \u2014 the histogram the p95 panel was retired for on 2026-09-16 (llama.cpp exports no latency histogram) is back with the radiance cutover. TTFT is what a fresh prefill costs; ITL is the per-token cadence an agent loop feels; e2e is the whole request.",
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"gridPos": {
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"h": 8,
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"w": 12,
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"x": 12,
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"y": 24
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},
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"datasource": {
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"type": "prometheus",
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"uid": "prometheus"
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},
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"fieldConfig": {
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"defaults": {
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"unit": "s",
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"custom": {
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"fillOpacity": 10,
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"lineWidth": 1
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}
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},
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"overrides": []
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},
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"targets": [
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{
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"expr": "histogram_quantile(0.95, sum by (le, job) (rate(vllm:time_to_first_token_seconds_bucket{job=~\"$job\"}[5m])))",
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"legendFormat": "{{job}} TTFT p95",
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"refId": "A"
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},
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{
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"expr": "histogram_quantile(0.95, sum by (le, job) (rate(vllm:inter_token_latency_seconds_bucket{job=~\"$job\"}[5m])))",
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"legendFormat": "{{job}} ITL p95",
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"refId": "B"
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},
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{
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"expr": "histogram_quantile(0.95, sum by (le, job) (rate(vllm:e2e_request_latency_seconds_bucket{job=~\"$job\"}[5m])))",
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"legendFormat": "{{job}} e2e p95",
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"refId": "C"
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}
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]
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}
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]
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}
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