The Dedicated
Inference Utility.

Purpose-built GPU infrastructure for mid-market AI companies. 1,000–20,000 GPU clusters, 100% reserved, silicon-agnostic, live in 4–6 months.

See How It Works
1–10K
GPU Clusters
100%
Reserved Model
4–6 Mo
Time to Live
$1.8B
Projected Revenue

The Infrastructure Gap

2,500 funded AI startups. 50–100 active infrastructure buyers. Zero purpose-built options for dedicated GPU clusters at this scale — until Meridian.

Hyperscalers

Too Big
  • 500 MW+ campuses
  • 2–4 year timelines
  • Enterprise contracts only

Neoclouds

Too Leveraged
  • Full-stack, GPU provider-locked
  • Single balance sheet risk
  • Training-optimized costs

Spot Markets

Too Volatile
  • No dedicated capacity
  • No SLA guarantees
  • 40–65% utilization volatility

Mid-market AI companies need 1,000–10,000 GPUs on 6–12 month timelines.

Nobody serves them. Until now.

A structural shift, not a cycle
$49.8B
GPU-as-a-Service market by 2032
36% CAGR
60–70%
Compute shifting to inference by 2027
vs training
50–100
Active infra buyers below 150 MW
Underserved today
$13B+
Funding across target pipeline
26 identified targets

Contracted. Asset-Light.
Purpose-Built.

Meridian delivers dedicated GPU clusters through a 100% reserved instance model. Every rack is contracted before deployment. Zero utilization risk.

100% Reserved

Zero Utilization Risk

Annual commitments at fixed rates. Revenue is guaranteed before we rack a single GPU — no spot exposure, no idle capacity. Every cluster is contracted before deployment begins.

Silicon Agnostic

Best Tokens/Watt

NVIDIA, AMD, TPU, FPGA. Ethernet-only (RoCE v2). Optimized per workload, never locked to one vendor.

Inference-Only

Optimized Economics

50–70% TDP draw. PCIe sufficient, Ethernet adequate. Lower power and cooling than training clusters.

Tier 1 Metros

Power Diversity

N. Virginia, Dallas, Phoenix, Sacramento. ERCOT + utility power across markets, not one campus bet.

OpCo / PropCo

Founder Control

Project-level equity via ring-fenced SPVs. Founders keep 100% OpCo control, IP, and customer relationships.

4–6 Month Deploy

Not 2–4 Years

GPUs and Ethernet drop into existing colo shells — no construction risk, no multi-year build. LOIs are secured before deployment begins, so capacity is live while competitors are still pouring concrete.

Own the GPUs.
Flex Everything Else.

We own the scarcest asset in AI — the GPUs — outright. Every other layer of the stack is delivered through best-in-class, swappable partners. Maximum control where it counts; maximum flexibility everywhere else.

What We Own

The GPU Assets

The most valuable, supply-constrained asset in the world today. Meridian owns the GPUs outright, held in ring-fenced SPVs, and controls how every cluster is allocated, contracted, and refreshed. That's the moat — everything else is deliberately flexible.

Flexible by design — partner-delivered, swappable layers
01

Construction & Staffing

Build & operate

Liquid-cooling retrofit, powered-shell buildout, 24/7 data-center operations, and greenfield construction — delivered by specialist build partners.

02

Network Fabric

Ethernet-only, RoCE v2

Ethernet-based inference fabric. No InfiniBand. Silicon agnosticism preserved end to end.

03

Asset Protection

Residual value & refresh

GPU residual-value programs salvage hardware at ~30% of initial CapEx at month 36, funding the refresh cycle and protecting the asset we own.

04

Infrastructure Monitoring

Full DCIM

Power, cooling, and environmental sensors across the stack — capacity planning and predictive analytics.

05

Silicon Supply

Multi-vendor, agnostic

NVIDIA, AMD, TPU, FPGA. Best tokens-per-watt per workload, with zero vendor lock-in.

The Result

Own the asset. Flex the stack. = the most performance-focused, vendor-neutral inference offering available today.

Why Meridian. Why Now.

A $49.8B market with no purpose-built solution for its fastest-growing segment. We built the infrastructure mid-market AI companies have been waiting for.

$49.8B
GPU-as-a-Service TAM by 2032, growing 36% a year — with 2,500+ funded AI startups and $13B+ of pipeline funding across 26 active targets, all needing dedicated infrastructure.

Training dominated the last cycle. Inference dominates the next one. The mid-market is where that shift lands first — and hardest.

The Problem

Hyperscalers won't touch sub-150 MW deals

Meridian

1,000–10,000 GPU clusters, purpose-sized

The Problem

Spot markets can't guarantee SLAs or dedicated capacity

Meridian

100% reserved model — zero utilization volatility

The Problem

2–4 year build timelines kill product roadmaps

Meridian

4–6 month deploy into existing colo shells

Silicon-agnostic Ethernet-only fabric No vendor lock-in Ring-fenced SPVs GPU-owned, asset-light 5-layer partner ecosystem

Your AI product deserves
dedicated infrastructure.

Dedicated GPU clusters deployed in 4–6 months. 100% reserved capacity, zero utilization risk, silicon-agnostic. Built for AI companies that can't afford to wait.