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 2026-07-08: context reduced to 128K on NVIDIA GPUs, parallel 2 on all GPUs, LiteLLM timeouts tuned (gemma 25→120s, qwen 40→90s), router fully deprecated — nginx routes /v1 → LiteLLM directly.	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
• port_conflict_detection: { status: "active" } — All 3 GPU wrappers detect ghost processes before binding

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        │
│ 128K ctx │  │ 128K ctx    │  │ 256K ctx  │  │ Watchdog     │
│ qwen3.6  │  │ gemma-4-12b │  │ ornith35B │  │ Prometheus   │
│ 27B-code │  │ :8080       │  │ :8080     │  │ exporter     │
│ :8080    │  │ :9400 (exp) │  │ :9400(exp)│  │ :9401        │
│ :9400    │  └─────────────┘  └───────────┘  └──────────────┘
└──────────┘

Current Model Assignments (2026-07-08)

Model	GPU	Host	VRAM	Ctx	KV Cache	Parallel	Batch/Ubatch	Status
qwen3.6-27B-code	RTX 3090	.8 (llm-gpu)	20.3/24GB (83%)	128K	turbo4	2	512/512	✅ healthy
gemma-4-12b	RTX 5070	.110 (ocu-llm)	9.4/12.2GB (77%)	128K	q4_0	2	2048/512	✅ healthy
ornith-1.0-35b	Strix Halo Vulkan	.15 (amdpve)	24.4/64GB (35%)	256K	q8_0	2	2048/512	✅ 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, context windows, parallel slots, active requests, circuit breakers, keys

health-check

1. Check GPU hardware: nvidia-smi (.8, .110) + amdgpu sysfs (.15 via /sys/class/drm/card0/device/)
2. Check llama-server processes: ps aux | grep llama-server on all 3 hosts
3. Check LiteLLM: curl http://192.168.68.116/health (expect "I'm alive!")
4. Check LiteLLM models: curl -H "Authorization: Bearer $MASTER_KEY" http://192.168.68.116/v1/models
5. Check LiteLLM timeouts: grep -n 'timeout:' /opt/inference-harness/litellm_config.yaml
   • gemma-4-12b: 120s, qwen3.6-27B: 90s, ornith-1.0-35b: 120s
   • global request_timeout: 300s, nginx proxy_read_timeout: 600s
6. Check AMD metrics: curl http://192.168.68.15:9400/metrics (Radeon 8060S, util%, VRAM, temp, power)
7. Check port conflicts: verify only one llama-server on :8080 per host
8. Verify agent keys: 9 keys in LiteLLM DB (GET /key/list)

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

Agent	CT	IP	Key	Access
Tanko	112	.122	sk-CggiHWlamQyShxWC3Hx6uw	SSH jerome
Mumuni	114	.123	sk-VrqCNlwUgzoNGOpikJ7nwQ	SSH root
Abiba	100	.24	sk-Qvzi4uYQBhlSK_XstEhcyQ	local (pi agent)
Tdunna	111	?	sk-6sbCNjz2T6lTVDBdlNHXsA	Zulip DM
Baggy	113	?	sk-krnw_zGBwvvL5b7l2t-s-A	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 (.24)	GPU fleet monitor v2.1.0 (with benchmarks)
/root/scripts/gpu_benchmark.py	pi (.24)	GPU inference benchmark module (tok/s tracking)
/root/scripts/gpu-saturation-watchdog.py	pi (.24)	Auto-restart stuck llama-server
/root/dashboard/gpu-fleet.html	pi (.24)	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 (2026-07-08): RTX 3090 at 20.3/24GB (83%), RTX 5070 at 9.4/12.2GB (77%), Strix Halo at 24.4/64GB (35%). Context reduced from 256K→128K on NVIDIA GPUs freed ~3.3GB (.8) and ~1.5GB (.110).
• All GPUs at --parallel 2 (2026-07-08): Fleet serves 6 concurrent requests (was 3). 2× throughput.
• RTX 3090 config: -c 131072 -ctk turbo4 -ctv turbo4 --parallel 2. No explicit batch flags (512/512 default). Service: /home/llmuser/llama-wrapper.sh.
• RTX 5070 config: --ctx-size 131072 --cache-type-k q4_0 --cache-type-v q4_0 --batch-size 2048 --ubatch-size 512 --parallel 2. Ubatch fixed 4096→512 (was inverted — ubatch > batch killed prompt throughput). Service: /home/llmuser/llama-wrapper.sh.
• LiteLLM timeout tuning (2026-07-08): gemma-4-12b 25→120s, qwen3.6-27B-code 40→90s, syslog-auto (qwen route) 40→90s. Nginx proxy_read_timeout: 600s. Global request_timeout: 300s. Config at /opt/inference-harness/litellm_config.yaml.
• 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.
• Port conflict detection (2026-07-05): All 3 GPU wrappers now detect ghost processes squatting port 8080 before starting. .8 and .110 use inline pre-start check in llama-wrapper.sh; .15 uses /usr/local/bin/port-cleanup.sh ExecStartPre. Replaces the blanket pkill -9 -x llama-server on .15 which would kill ALL llama-server instances regardless of port. Ghost detection was the root cause of .8 crash-looping for 27+ restarts (stale pid 25836 squatting 8080 after OOM kill).
• 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)	qwen3.6-27B-code	75	305	74	6
RTX 5070 (.110)	gemma-4-12b	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.

Agent Config Implications (2026-07-08)

With NVIDIA GPUs at 128K context:

• Agents using syslog-auto (50/50 qwen+ornith): keep context_length: 262144 — ornith supports it, Litellm fallbacks handle qwen overflow
• Agents using qwen3.6-27B-code directly: set context_length: 131072 and max_tokens: 4096 per thermal safety rule
• Agents using gemma-4-12b directly (auxiliary tasks): set context_length: 131072
• Compression threshold at 0.65: fires at ~170K for syslog-auto (262K ctx), ~85K for direct qwen/gemma (128K ctx)
• All Hermes clients MUST set max_tokens: 4096 — first line of defense before server-side -n 8192 cap
• Port 8080 is used on all 3 GPU hosts (not 8090 as previously documented)