How I built a 6-node 12-GPU on-prem AI cluster running 1000+ agents

Turbo31150 — Tue, 19 May 2026 20:10:07 +0000

TL;DR — 6 machines, 12 GPUs, 1,000+ concurrent agents, P95 18 ms, voice <300 ms, 280,741 lines of Python, 44 MIT repos. Vs Azure OpenAI: 7-month break-even on a 50K€ deployment.

Why I built this

I'm Franck. Toulouse, France. Over 3 years I paid roughly €280,000 to Azure + OpenAI before doing the math properly:

Latency: 1.2s voice round-trip — incompatible with the voice-first UX I wanted.
Compliance: customer data on US servers. Not GDPR-native, just GDPR-compliant-on-paper.
Quotas: random throttling at the worst times.
Lock-in: Azure outage = my product offline.

I decided to rebuild everything on-prem. This is the result.

The cluster

6 machines, 3 tiers, 12 GPUs total, <5ms inter-node latency.

Tier 1 — GPU compute (heavy inference)

M1 "La Créatrice" — Ryzen 5700X3D, 6× RTX 3080+, 46 GB RAM. Primary LLM node, runs qwen3.5-9b, qwen3.5-35b-a3b, deepseek-r1, the Claude 4.5/4.6 distillations, and the Whisper CUDA pipeline.
M2 "Le Forge" — multi-GPU NVIDIA, secondary inference, failover from M1 in 1.3s.

Tier 2 — CPU/RAM (orchestration, memory)

M3 "Le Cerveau" — high-RAM CPU node. PostgreSQL + Redis + Pinecone. Runs the orchestrator, the 3-quorum consensus engine (M1+M2+M3), and the analytics/monitoring agents.

Tier 3 — production / work

M4 "Bridge Windows" — Windows 11, 2 GPUs, trading bot live.
M5 "Interface Relay" — Linux i5-6500, 15 GB RAM. Dev interface, 15+ MCP servers, Claude Code.
M6 "Mobile Ops" — laptop. SSH + VPN. Client demos and on-site ops.

The 9 layers I added on top of Ubuntu

L9 — Vocal / conversational (Whisper CUDA STT, Piper TTS, wake word, 50+ languages)
L8 — Multi-agent orchestration (MCP-native, consensus engine)
L7 — Trading consensus engine (multi-model voting GPT/Gemini/Claude)
L6 — Browser + web automation (Chrome DevTools Protocol)
L5 — MCP tool registry (88+ handlers)
L4 — GPU cluster management (Docker Swarm, failover <2s)
L3 — Domino pipeline engine (835 chains)
L2 — systemd service layer (98 units)
L1 — Linux boot integration (GRUB hooks, ZRAM, kernel params)

Real numbers

Metric	Value
Concurrent agents	1,000+
P95 latency (cluster internal)	18 ms
Voice pipeline end-to-end	<300 ms
Aggregate throughput	67 tok/s
Python lines	280,741
Public repos	44 (all MIT)

Cost comparison (1M tokens/day, team of 10)

Provider	€/month	P95	Concurrent agents	Data residency
Azure OpenAI	1,500	800ms-3s	~20	US
AWS Bedrock	1,800	700ms-2.5s	~15	US
Mistral Cloud	800	400-800ms	~30	EU
JARVIS OS	0	18 ms	1,000+	Air-gapped

For a 50K€ turn-key deployment, break-even vs Azure is 7 months, and the marginal cost is zero after that.

What I sell now

JARVIS OS turn-key — 20K€ to 250K€ depending on scope.
62 PDF trainings — from €39, 293h of content based on production code (+48 private).
IA infra audit — €1,500, report in 48h.
1-to-1 mentorship — €250/h.
Fractional CTO — TJM €1,000-1,150 / CDI €85-95K. Toulouse / remote.

Honest weaknesses

Consensus voting is empirical. No formal verification of the agreement function.
Tier-2 failure (M3 down) is the weakest scenario — orchestrator dies, cluster keeps inferring but loses persistent memory.
MCP protocol bet — if Anthropic deprecates parts of MCP, I have 88 handlers to refactor.
kWh-per-token efficiency — cloud probably wins on aggregate watts/token, on-prem wins on marginal cost.

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