BUILD TIMELINE
3.2–4years
Industry median from broken ground to first MW serving inference.
APPLYSTUDYAGREEMENTBUILDPOWER ON
The AI economy is running on broken infrastructure.
Four metrics explain every missed AI deployment, stranded GPU, and 24-month grid queue. The stack we inherited from cloud doesn't work at AI physics.
CAPEX PER MEGAWATT
$155–170million
Mostly concrete, substation, and cooling — not compute.
SITE + EPC$52M
GRID + SUBSTATION$34M
COOLING + WATER$28M
GPUs + NETWORK$41M
GPU MEMORY USEFUL
12–20percent
The rest is duplication, KV-cache overhead, and idle VRAM.
80–90%VRAM WASTED
TIME-TO-POWER
30–36months
Utility interconnect queue for a 100 MW+ greenfield site.
GRID QUEUE · 80% ACTIVE BUILD · 12%
The industry is building 2020-era data centers for 2030 AI. PODOS starts over.
02·THE SOLUTIONF-01 F-02 F-03 F-04 F-05 F-06
Factory-built AI compute pods.
PODOS AI designs modular compute pods that arrive as engineered infrastructure units — built for power, cooling, compute density, deployment speed, and operational flexibility.
Modular pod architecture
Engineered infrastructure units, not custom one-offs. A repeatable design with predictable interfaces means faster site work, simpler operations, and a lower-variance commissioning timeline.
Factory-built construction
Pods are produced in a controlled environment with consistent quality, then shipped as completed assemblies. Off-site construction runs in parallel with site preparation.
High-density compute
Dense GPU and accelerator configurations engineered for AI workloads, with the cooling and power to match.
Deployable at facilities
Designed to be placed where compute is actually needed — campuses, sites, edge locations.
Fast commissioning
Standardized hookups for power, network, and cooling cut weeks off on-site bring-up.
Secure, controlled enclosure
Hardened pod chassis with controlled access, environmental sealing, and integrated monitoring.
Deploy one megawatt in 90–120 days.
Not four years.
PODOS is a factory-built modular AI supercomputer. Power, cooling, networking, fire, seismic — integrated and pressure-tested before it leaves California. Sites prepare the pad. The pod brings the data center.
POD-0042 · ASSY3D · LIVE GEOMETRY · DRAG OR SCROLL
Streaming rack geometry…
POD-0042 · REV·E · CONFIDENTIAL — SEED · 2026
8× DENSITY. LIQUID COOLED. BUILT TO SCALE.
High-density GPU infrastructure in a modular, liquid-cooled pod. Click any callout to explore the spec.
PWR IN
COOL IN
COOL OUT
NET FIBER
EXHAUST
- 8× HIGH-DENSITYGPU MODULES
- CLOSED-LOOPLIQUID COOLING
- HIGH CAPACITYPOWER DELIVERY
- HIGH-SPEEDNETWORK FABRIC
- RUGGED. SECURE.BUILT TO DEPLOY.
DEPLOY · 90–120 DAYS · FACTORY TO FIRST MW
DEPLOY · 90–120 DAYS · FACTORY TO FIRST MW
D+0Order placedSite engineering kickoff.
D+30Factory completePod integrated, burned-in, shipped.
D+60Site + arrivalConcrete pad cured, pod craned in.
D+90–120Serving inferenceFirst megawatt online.
Same DNA, two scales.
Unit economics prove at 1 MW · cluster economics unlock at 20 MW.
PROD-01 · PODOS POD · 1 MW · UNIT
PROD-02 · MEGA SILO · 20 MW · CLUSTER
Infrastructure designed for faster, simpler deployment.
Every PODOS module is engineered to reduce deployment friction, increase resilience, and improve infrastructure economics.
6 surfacesINSULATED
Thermos enclosure
Multi-layer foam core + reflective vapor barrier on all six surfaces. Thermal delta held steady regardless of climate — Arctic field to Phoenix tarmac, same PUE.
$57K/yr/pod reclaimed
ORC heat engine
Organic Rankine Cycle on the waste-heat loop recaptures 60–110 kW as grid-synchronous electricity. Adds revenue per pod without any additional footprint.
0GRID DEPENDENCY
Off-grid ready
Solar roof + battery bank + backup generator integrated. Deploy to remote edge sites without fiber or utility interconnect — same 90-day timeline, anywhere on the map.
0 galWATER · CONCRETE
Zero water · zero concrete
Closed-loop direct-to-chip liquid cooling means no cooling towers, no water-rights negotiation, no slab permits. The pod lands on gravel or asphalt and starts serving inference.
From factory to facility.
05·USE CASES
Built for organizations that need controlled AI infrastructure.
From research and clinical environments to manufacturing floors and edge sites, PODOS pods deploy AI compute where it's actually used.
U-01·01 / 08
Enterprise AI teams
Internal compute for training, inference, and AI product development — close to the data.
U-02·02 / 08
Healthcare facilities
On-prem AI infrastructure for clinical, imaging, and research workloads inside the compliance boundary.
U-03·03 / 08
Universities & research labs
Dedicated compute for research groups without competing for shared cluster time.
U-04·04 / 08
Manufacturing sites
On-floor compute for industrial AI, vision systems, and process intelligence.
U-05·05 / 08
Financial institutions
Inference and modeling capacity inside controlled, audit-friendly infrastructure.
U-06·06 / 08
Government & secure environments
Pods for restricted facilities and operations that require physical and network control.
U-07·07 / 08
Edge AI deployments
Compute placed close to where data is generated — closer to operations, lower latency.
U-08·08 / 08
Supplemental capacity
Add headroom to existing infrastructure without waiting years for new construction.
06·MANUFACTURING
Built through modular manufacturing discipline.
PODOS brings modular construction principles to AI infrastructure. Pods are engineered in a controlled production environment and delivered for site deployment.
- Factory-built approach.Pods are produced as engineered infrastructure products, not site-fabricated assemblies.
- Consistent production process.A repeatable build sequence reduces variability and improves predictability.
- Faster deployment path.Off-site construction runs in parallel with site preparation, compressing the overall timeline.
- Controlled quality.Production-environment QA replaces field rework as the primary quality lever.
- Repeatable pod architecture.A common architecture across deployments simplifies operations, spares, and upgrades.
07·ENGINEERING
D-01 D-02 D-03 D-04 D-05 D-06
Engineered for deployment, density, and control.
PODOS pods are built around six engineering pillars that together define what a deployable AI compute unit is supposed to be.
Compute density
Engineered to host high-performance accelerator configurations within a single deployable unit.
Cooling architecture
Thermal design matched to AI compute load, sustained under continuous operation.
Power readiness
Power distribution sized for high-density compute and configured for site interconnect.
Physical security
Hardened enclosure with controlled access and environmental protection.
Operational flexibility
Configurable for different compute profiles, deployment contexts, and operational models.
Deployment speed
Designed end-to-end for fast site commissioning and compressed time-to-capacity.
Engineered systemNot assembled.
Built for uptimeContinuous by design.
Modular by designAdaptable to any site.
Commissioned fasterFrom site to service.
08·TEAM
T-02
T-03
T-05
Meet the operators behind modular compute
A small team with deep operational experience across data center construction, industrial manufacturing, and AI infrastructure.
T-01
Josef Elimelech
Founder & InventorCreator of all 76+ patent claims across both platforms — inventor of record on every USPTO filing. Technical architect of PODOS Pod, MEGA SILO, Syntropic, and Optimus.
Greg McNulty
Chief Executive OfficerFormer Microsoft executive. Enterprise-scale operational leadership and institutional investor relationships taking PODOS AI from invention to global market.
Mike Sherman
Chief Technology OfficerBuilt the Syntropic GPU benchmark suite — validated 99.6% quality preservation on Mistral-7B across 3 GPU platforms. Engineering lead for Optimus and Syntropic.
T-04
Barbara Liebeck
VP Sales & Business Dev.Enterprise account management across AI infrastructure. Leads the customer pipeline for EcoSynQ, the Israel market, and hyperscaler prospects.
Rafael Smadja
Graphic Designer & WebBrand identity, thesyntropic.com, and PODOS AI web presence. Translates the technical platform into investor-grade visual communications.
09·GET IN TOUCH
Need AI compute capacity without a multi-year infrastructure timeline?
Talk with PODOS AI about modular compute pod deployment options for your facility.
Let's Work Together
The fastest path to a deployment conversation
Reach out by phone, email, or stop by the factory. We respond to every inquiry within 72 hours.
PODOS-AI · CONTACT·Modular AI compute infrastructure
OPEN · TAKING DEPLOYMENT INQUIRIES