I built JARVIS OS: 1000+ autonomous AI agents, on-prem, <300ms voice latency — here's the full architecture

Turbo31150 — Fri, 22 May 2026 07:01:53 +0000

I've spent the last 3 years building JARVIS OS — a fully autonomous, on-premise AI infrastructure that runs 1000+ autonomous agents simultaneously, processes voice in under 300ms, and costs a fraction of cloud alternatives.

Today I'm sharing the full architecture, the key decisions, and the lessons learned.

→ Live site & full details: jarvis-delmas.netlify.app

What is JARVIS OS?

JARVIS OS is a distributed AI operating system designed to run entirely on your own hardware — no OpenAI, no Azure, no data leaving your infrastructure.

Key production numbers:

1000+ autonomous agents running simultaneously
<300ms voice latency (Whisper CUDA optimized)
835 auto-healing pipelines with circuit-breakers
280,741 lines of Python across 60 MIT-licensed repos
12 GPUs in cluster
Benchmark: 81.6/100 (record session: 97/100)
-72% infrastructure cost vs equivalent cloud setup

The 9-Layer Architecture

Layer 1: Hardware (GPU cluster, NVMe, InfiniBand)
Layer 2: OS + Virtualization (Linux, Docker, CUDA)
Layer 3: LLM Engine (LM Studio, Ollama, multi-model routing)
Layer 4: Memory System (working → episodic → semantic → procedural)
Layer 5: Agent Orchestration (OpenClaw Gateway, 1000+ agents)
Layer 6: MCP Toolkit (88 handlers, 20+ connectors)
Layer 7: Pipeline Engine (835 Domino auto-healing pipelines)
Layer 8: Voice Interface (Whisper → LLM → TTS <300ms)
Layer 9: External APIs (TradeOracle, Telegram, GitHub)

5 Architectural Decisions That Made the Difference

1. On-Premise by Design

Most teams start with cloud and try to migrate later. We started on-prem from day one.

Result: zero cold start, zero API rate limits, GDPR-native.

Cost comparison:

Cloud equivalent: €50,000–500,000/year
JARVIS OS: one-shot deployment + maintenance

2. Protocol-First with MCP

Instead of direct integrations, everything goes through the Model Context Protocol (MCP).

Our MCP Toolkit has 88 handlers connecting: filesystem, GitHub, Notion, Slack, PostgreSQL, Redis, vector DBs, Telegram, browser automation, and custom CUDA endpoints.

Any new agent instantly has access to all 88 capabilities.

3. 4-Layer Memory Architecture

# Memory hierarchy in JARVIS OS
working_memory    = RedisCache(ttl=3600)           # Current context
episodic_memory   = PostgreSQL(table="episodes")   # Recent events  
semantic_memory   = ChromaDB(collection="knowledge") # Facts & concepts
procedural_memory = FileSystem(path="./skills/")   # Learned skills

The Π-vectorial compression achieves a 15:1 compression ratio — 15x more context in the same token budget.

4. Auto-Healing Pipelines

All 835 pipelines have built-in circuit-breakers and 13 auto-trigger mechanisms.

@circuit_breaker(failure_threshold=3, recovery_timeout=60)
@auto_retry(max_attempts=3, backoff_factor=2)
async def run_pipeline(pipeline_id: str, context: dict):
    # Pipeline execution with automatic recovery
    ...

5. Voice Pipeline Under 300ms

Stack: Whisper (CUDA) → LLM routing → TTS → audio output

Optimizations:

CUDA-optimized Whisper with float16 precision
Streaming inference (token-by-token TTS)
Wake word detection on a separate thread
Audio buffer pre-warming

Average benchmark: 247ms end-to-end on P95 GPU.

The Open-Source Stack

LLMs:          Ollama, LM Studio, GGUF models
Orchestration: OpenClaw Gateway (custom, MIT)
Memory:        PostgreSQL + pgvector, ChromaDB, Redis
Voice:         Whisper CUDA, custom TTS pipeline
MCP:           88 custom handlers
Containers:    Docker (10 services), NVIDIA GPU Operator
Monitoring:    Prometheus + Grafana
Languages:     Python (primary), Rust (performance-critical)

All 60 repos available on GitHub under MIT license:
👉 github.com/Turbo31150

Real-World Modules Running on JARVIS OS

TradeOracle — 7 LLMs in consensus for crypto/equity signals
Healthcare Multi-Agent — FHIR-compatible medical transcription
Domino Engine — 835 self-healing data pipelines
OpenClaw Gateway — orchestrates 1000+ agents in production

Key Lessons After 3 Years

Start on-prem — cloud migration is 10x harder than building on-prem from day 1
Protocols over integrations — MCP saved us from integration hell
Memory is the hardest problem — 80% of agent failures are memory coherence issues
Voice latency is binary — users accept <300ms, reject >500ms
Auto-healing or nothing — production pipelines need circuit-breakers from day 1

Learn to Build Your Own

If you want to build a similar system, I've documented everything:

🎓 Claude Code Mastery — 13 lessons, build your own agent system in 4 weeks

Module 1: FREE → your first agent in 30 minutes
Bundle M2+M3: €477 early-bird (vs €797)
14-day "Agent or Refunded" guarantee

📚 62 PDF formations — from beginner to JARVIS expert
🚀 Clé-en-main deployment — I deploy on your hardware in 2–8 weeks

👉 jarvis-delmas.netlify.app

Questions? I answer everything in the comments.
GitHub: github.com/Turbo31150 — 60 repos, all MIT

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.

DEV Community: Turbo31150