prose-contracts/gpu-fleet.prose.md

kind, name, description, agent, triggers

kind	name	description	agent	triggers
responsibility	gpu-fleet	Manages the GPU inference fleet across all hosts. Handles model deployment, registration, health checks, LiteLLM sync, agent key management, GPU saturation watchdog, Prometheus/Grafana monitoring, and self-healing. Current as of 2026-06-30 post-migration: nginx routes /v1 → LiteLLM, router runs behind LiteLLM on :9000 (internal-only).	abiba	on model add/remove on GPU health degradation on agent key rotation on router restart (roster must be loaded)

Maintains

• gpu_roster: { models: map, hosts: map } — Single source of truth for all GPU models
• router: { status: "healthy", roster_loaded: bool, models: array }
• litellm: { status: "healthy", keys: array, models: array }
• agent_keys: { agent: api_key } — All agent API keys registered in LiteLLM DB
• health: { gpus: array, circuit_breakers: array } — Fleet-wide health state
• monitor: { status: "running", version: "2.0.0" } — GPU monitor server on pi (:9100)
• watchdog: { status: "running" } — GPU saturation watchdog (restarts stuck llama-server)
• benchmarks: { tok_per_sec: map, baseline: map, history: array } — Inference speed benchmarks tracked over time
• grafana: { status: "running", dashboards: ["gpu-fleet"] } — Grafana on CT 116 (:3001)
• prometheus: { status: "running", targets: 5 } — Scrapes GPU :9400 exporters + LiteLLM

Fleet Topology (Current — June 2026)

┌──────────────────────────────────────────────────────────────────┐
│  CT 116 (192.168.68.116) — Inference Harness Host                │
│                                                                  │
│  nginx:80 (entrypoint)                                           │
│  ├─ /v1/*       → harness-litellm:4000   (API requests)         │
│  ├─ /admin/*    → harness-litellm:4000   (admin endpoints)       │
│  ├─ /dashboard/ → harness-dashboard:3000 (harness UI)            │
│  ├─ /litellm/*  → harness-litellm:4000   (LiteLLM UI + API)     │
│  ├─ /health/*   → harness-litellm:4000   (health probes)         │
│  └─ /gpu/*      → 192.168.68.24:9100     (fleet monitor)         │
│                                                                  │
│  Containers:                                                     │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐        │
│  │ LiteLLM  │  │ Router   │  │Dashboard │  │ Grafana   │        │
│  │ :4000    │─▶│ :9000    │  │ :3000    │  │ :3000     │        │
│  │ keys+sync│  │internal  │  │ harness  │  │ Prometheus│        │
│  │ fallback │  │only!     │  │ UI       │  │ data src  │        │
│  └──────────┘  └───┬──────┘  └──────────┘  └──────────┘        │
│                    │                                             │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐                       │
│  │PostgreSQL│  │  Redis   │  │Prometheus│                       │
│  │ :5432    │  │ :6379    │  │ :9090    │                       │
│  └──────────┘  └──────────┘  └──────────┘                       │
└────────────────────┼─────────────────────────────────────────────┘
                     │
     ┌───────────────┼───────────────┬──────────────────┐
     │               │               │                  │
┌────▼─────┐  ┌──────▼──────┐  ┌────▼──────┐  ┌───────▼──────┐
│ CT 8     │  │ CT 110      │  │ CT 15     │  │ pi (.24)     │
│ RTX 3090 │  │ RTX 5070    │  │ Strix Halo│  │ GPU Monitor  │
│ 24GB     │  │ 12GB        │  │ 64GB UMA  │  │ :9100        │
│          │  │             │  │ llama-srv │  │ Watchdog     │
│ qwen3.6  │  │ gemma-4-12b │  │ ornith35B │  │ Prometheus   │
│ 27B-code │  │ :8090       │  │ :8080     │  │ exporter     │
│ :8090    │  │ :9400 (exp) │  │ :9400(exp)│  │ :9401        │
│ :9400    │  └─────────────┘  └───────────┘  └──────────────┘
└──────────┘

Current Model Assignments

Model	GPU	Host	VRAM	Status
qwen3.6-27B-code	RTX 3090	.8 (llm-gpu)	23.4/24GB	✅ healthy
gemma-4-12b	RTX 5070	.110 (ocu-llm)	11.2/12.2GB	✅ healthy
ornith-1.0-35b	Strix Halo Vulkan	.15 (amdpve)	22.7/64GB	✅ healthy

Operations

add-model

1. Download model file from Hugging Face or source
2. Check disk space + GPU VRAM compatibility
3. Start llama-server via systemd service on GPU host
4. Add to /opt/inference-harness/gpu_roster.yaml (or router env vars)
5. Hot-reload or restart router
6. Add model to LiteLLM config (model_list + fallbacks)
7. Generate agent keys for new model access via /key/generate
8. Add Prometheus scrape target for the new GPU exporter
9. Verify end-to-end: LiteLLM → Router → Model

remove-model

1. Drain active requests (wait for active=0)
2. Remove from LiteLLM config
3. Remove from router config
4. Stop llama-server (systemd)
5. Remove Prometheus scrape target
6. Cleanup model files (optional)

heal

1. Check all GPUs via router internal :9000/health/unified
2. Check LiteLLM health via nginx :80/litellm/health/liveliness
3. Reset stuck circuit breakers if idle (Redis)
4. Restart dead llama-server instances via SSH
5. Flush Redis active counters if stale
6. Verify GPU monitor server is running on pi (:9100)
7. Verify watchdog is running on pi
8. Restart router if roster not loaded (check logs for STARTUP ROSTER)
9. Reload roster via POST :9000/admin/roster/reload if available

sync-keys

1. List all agent keys in LiteLLM DB via GET /key/list
2. Compare against expected agent list: [tanko, mumuni, abiba, tdunna, baggy, kagenz0]
3. Generate missing keys via POST /key/generate with unlimited budget
4. Update agent configs — /etc/environment LITELLM_API_KEY
5. Send Zulip DM to agents that can't be reached via SSH
6. Verify each key with test request through full chain
7. Document keys in knowledge graph

list

Show full fleet status: GPUs, models, VRAM, active requests, circuit breakers, keys

Agent Keys (LiteLLM DB — Current 2026-06-30)

Agent	CT	IP	Key	Access
Tanko	112	.122	sk-3ZsdWJbbNSo9zSnJN2OsJw	SSH jerome
Mumuni	114	.123	sk-XY2aUfvy2BIs6kp1ZPh6VA	SSH root
Abiba	100	.65	sk-Qvzi4uYQBhlSK_XstEhcyQ	local (pi agent)
Tdunna	111	?	sk-lDTsWy7H3T19UwUFEN6JSg	Zulip DM (regenerated 2026-07-01 — old sk-eb3e6... was router key, not LiteLLM)
Baggy	113	?	sk-Prx_vRXYb2VEzDmhPCbNeg	no SSH
Kagenz0	105	?	sk-Dh4CDkaHebMLEp8qqq20qA	no SSH

Key update procedure: Update /etc/environment → LITELLM_API_KEY=sk-... → restart Hermes. If no SSH access, send Zulip DM via abiba-bot.

Configuration Files

File	Host	Purpose
/opt/inference-harness/docker-compose.yml	CT 116	All containers (router, litellm, nginx, postgres, redis, dashboard)
/opt/inference-harness/litellm_config.yaml	CT 116	LiteLLM proxy config (models, fallbacks, timeouts)
/opt/inference-harness/router/router.py	CT 116	Router source (builds via compose)
/etc/nginx/nginx.conf	CT 116 (nginx container)	Routes /v1→LiteLLM, /dashboard/, /litellm/, /health
/opt/monitoring/prometheus.yml	CT 116	Prometheus scrape config (5 targets)
/root/scripts/gpu-monitor-server.py	pi (.65)	GPU fleet monitor v2.1.0 (with benchmarks)
/root/scripts/gpu_benchmark.py	pi (.65)	GPU inference benchmark module (tok/s tracking)
/root/scripts/gpu-saturation-watchdog.py	pi (.65)	Auto-restart stuck llama-server
/root/dashboard/gpu-fleet.html	pi (.65)	Live HTML dashboard
/etc/systemd/system/llama-server.service	.8, .110	llama-server daemons (Nvidia GPUs)
/etc/systemd/system/ornith-server.service	.15 (amdpve)	llama-server daemon (Vulkan, Strix Halo). Note: llama-server.service and llama-server@.service are masked on .15 to prevent port 8080 collisions.

Prometheus & Grafana

Component	URL	Details
Grafana	http://192.168.68.116:3001/	admin / syslog-grafana-2026
GPU Dashboard	http://192.168.68.116:3001/d/gpu-fleet	Gauges + time series
Prometheus	http://192.168.68.116:9090/ (internal)	5 scrape targets
GPU Exporters	:9400/metrics on .8, .110, .15	NVIDIA/AMD GPU metrics
Router Exporter	:9401/metrics on .24	Router + LiteLLM metrics

Known Issues & Watch Points

• Router startup race: Compose router.py doesn't call load_roster(). Reload thread sleeps 30s first. Fix: trigger roster reload via SSH after restart, or rebuild image with startup load_roster().
• LiteLLM /metrics: Requires auth. Prometheus uses /health/liveliness as workaround.
• VRAM: RTX 3090 at ~9.6/24GB (60% free), RTX 5070 at ~6.5/12GB (46% free). Healthy.
• RTX 3090 optimization: --parallel 2 --batch-size 4096 — doubled concurrent throughput.
• RTX 5070 optimization: Removed draft model freed ~2GB VRAM for vision tasks. --parallel 2.
• Strix Halo GPU: Vulkan is the working backend (ROCm/HIP path abandoned — HSA runtime blocked on Debian 13). Build at /root/llama.cpp/build-vk/, commit 4fc4ec5 (2026-07-01), ggml 0.15.3 shared-lib arch. Mesa RADV 25.0.7, KHR_coopmat fast path active. ~70 tok/s gen, 532 tok/s prompt. Service: ornith-server.service on port 8080, 256K context, flash-attn + q8 KV.
• Strix Halo thermal safeguard (2026-07-02): ornith-server.service has -n 8192 (hard generation cap per request). Without it, --predict defaults to -1 (infinity) — a runaway request from .123 (Mumuni) decoded 39,868 tokens over 24 min, pushing Tctl to 98°C (crit 89.8°C) and throttling 70→29 t/s. The cap bounds worst-case generation to ~5 min. Do NOT remove -n without a replacement ceiling. Sustained load hits ~84°C even at 92s; the APU is fanless/low-flow. Clients MUST also set max_tokens.
• Port 8080 firewall: amdpve iptables restricts 8080 to 192.168.68.116 (LiteLLM/router host) only. All inbound connections are from .116 (LiteLLM proxied via nginx). Localhost curls hang (SYN dropped). Always test from .116.
• Router sidecar fallback: router.py check_gpu_health() now probes GPU /health directly when sidecar at :8090 is absent. Sidecar JSON exporters not deployed on any GPU host — router relies on GPU-direct fallback.
• Router GPU_MOE_URL bug (fixed 2026-07-01): docker-compose had GPU_MOE_URL=.110:8080 (gemma host) instead of .15:8080 (amdpve). Corrected.
• Alert migration: All alerts now go to #agent-hub topics (alerts-gpu, alerts-pm2, alerts-infra) instead of DMs. Cross-agent visibility enabled.
• tok/s benchmarks: Measured every 5 min via LiteLLM proxy. Baselines tracked with 30%/50% degradation thresholds.
• NetBird 502: Tanko routes through NetBird for litellm.sysloggh.net. Use direct IP if NetBird down.

GPU Inference Benchmarks (Current)

GPU	Model	Gen tok/s	Prompt tok/s	Baseline	Samples
RTX 3090 (.8)	gemma-4-12b	75	305	74	6
RTX 5070 (.110)	qwen3.6-27B-code	75	323	75	6
Strix Halo (.15)	ornith-1.0-35b	70	532	70	6

Benchmarks run through LiteLLM proxy (192.168.68.116:4001) every 5 minutes. Degradation alerts fire at 30% (warning) and 50% (critical) below baseline. History stored at /root/data/toks-history.json with 7-day rolling window.

Note (2026-07-01): Strix Halo prompt tok/s jumped 209→532 after Vulkan rebuild (cooperative-matrix fast path now active on GFX1151). Baseline may need re-calibration.