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GPU-Accelerated PostgreSQL

"Cluster performance. Single-node simplicity."

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GPU Accelerated Postgres is Here


10B+ Row Tables Now Common

Traditional systems can't handle enterprise analytical data

$2M+ Annual Warehouse Costs

Distributed clusters require massive infrastructure spend

30%+ YoY Cost Growth

Cloud warehouse spend spiraling with no end in sight

Postgres is the #1 database among AI professionals — 59.5% developer adoption (Stack Overflow 2025)

The Future of Postgres on NVIDIA


PostgreSQL Extension

GPU-accelerated analytics that stays in the Postgres ecosystem. No migration, no retraining. Standard extension with no code forks.

NVIDIA SCADA Technology

GPU-direct storage access eliminates CPU bottlenecks. First to productize for Postgres.

Single-Node Scale

Performance that rivals multi-node clusters without the operational complexity.

DRAM IOPs for NVMe Costs

GPU-Direct storage fabrics can saturate NVMe systems, beating DRAM IOPs.

BEFORE 16 Node Cluster

Complex • Expensive • Fragile

AFTER 1 Node + cupug

Simple • Affordable • Reliable

How It Works


Traditional (CPU-Centric)

  • CPU orchestrates all storage I/O
  • Bulk reads and writes only
  • GPU idles waiting on CPU for data
  • Network shuffle between nodes dominates query time

cupug (GPU-Centric via SCADA)

  • GPU threads directly initiate storage I/O
  • Fine-grained, sparse reads — fetch only bytes needed
  • Massive thread parallelism hides storage latency
  • No network shuffle — all data local on NVMe
Result: Fine-grained, on-demand data access with massive parallelism on random-access workloads

Key Use Cases


  • Ad-hoc queries on 10B+ row tables
  • Real-time dashboards without pre-aggregation
  • ML feature store with historical depth
  • Hybrid OLTP/OLAP workloads on a single server
  • Data-dependent queries without I/O amplification
  • Tick-level financial data and risk modeling
  • CDR and network telemetry analytics
  • Clickstream and recommendation pipelines
  • Genomic and clinical trial queries
  • IoT sensor telemetry and predictive maintenance

TCO Comparison


Metric 16-Node Cluster cupug (1 Node) Advantage
Compute Cores 2,048 80,000 39x
Data Access Rate 3,200 GB/s 32,000 GB/s (HBM) 10x
Interconnect 100 Gbps 2,000 Gbps 20x
Storage IOPs 3-4M 40-200M 50x
CLUSTER ANNUAL TCO $850K-$1.15M

Compute + Storage + Operations

COST REDUCTION 8x

Better performance, fraction of cost

CUPUG ANNUAL TCO $106K-$150K

Single server + 4x B200 GPUs

Target Customers


Financial Services

Tick data, risk modeling, real-time compliance

Telecommunications

CDR analytics, network telemetry

E-commerce / AdTech

Clickstream, recommendations, ML features

Life Sciences

Genomic queries, clinical trials

IoT / Industrial

Sensor telemetry, predictive maintenance

Logistics / Supply Chain

Route optimization, inventory forecasting, tracking

Get Early Access

Join the waitlist for the cupug beta.