Replace Your Analytics Cluster With One GPU Server

Cluster performance. Single-node simplicity.

10x Faster

8x Cheaper

Standard PostgreSQL

Get Early Access See the Numbers

The $50B Cluster Problem

10B+ Row Tables Now Common

Traditional systems can't handle enterprise analytical data without clustering.

$2M+ Annual Warehouse Costs

Distributed clusters require massive infrastructure spend and staff resources.

30%+ YoY Cost Growth

Cloud providers set the price hikes, not you.

The Hidden Cost: Operational Complexity

Distributed clusters don't just cost more upfront, they spiral out of control. Every node added multiplies failure modes. High availability requires 3x replication, dedicated SRE teams, and 24/7 on-call rotations. Network partitions, split-brain scenarios, and cross-node coordination become daily firefights. What starts as a "scalable solution" becomes a full-time job for expensive specialists, all to keep the cluster from falling over.

Ditch the Clusters Costs with cupug

PostgreSQL Extension

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

GPU Direct 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.

TCO Comparison

Metric	8-Node CPU Cluster	cupug (1 Node 2x B200)	Advantage
CUDA Cores	1,024	33,792	33x
Memory Bandwidth	1,600 GB/s	16 TB/s (HBM3e)	10x
Node Interconnect	100-200 Gbps	1.8 TB/s (NVLink 5)	10x
Storage IOPs	1-2M	10-20M (10x NVMe)	10x

CPU CLUSTER ANNUAL TCO $400K-$550K

Compute + Storage + Operations

COST REDUCTION 8x

Better performance, fraction of cost

CUPUG ANNUAL TCO $55K-$65K

Single server + 2x B200 GPUs

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)

GPU direct 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

Storage Types

Row Storage

GPU-accelerated: OLTP, Joins, Row Operations

Use Standard heap tables from GPU
Full ACID transactions
Row-level joins and lookups
OLTP and mixed workloads

Column Storage

GPU-accelerated: Analytics, OLAP, Bulk Compute

GPU-accelerated columnar scans
GPU-direct NVMe reads
OLAP and data warehouse queries
Columnar compression (10–20x ratio)

Matrix Storage

GPU-accelerated: Matrix and Graph Workloads

Dense matrix operations via cuBLAS
Sparse matrix operations via cuSPARSE
Graph traversal via cuGraph
cuVS vector similarity search

Key Use Cases

Ad-hoc queries on 10B+ row tables
Massive-scale Vector Search and RAG
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

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

Pricing

Core

1-GPU System

Fully hosted

Single GPU
Local NVMe storage
Full SQL analytics acceleration
Heap & column block storage
Email support