GPU Training Active · 3 Models Running

BoldComp Africa
AI Climate Intelligence Infrastructure

Geospatial AI platform training climate models on satellite data — empowering African communities with real-time risk monitoring and inference at scale.

0

African Countries

0.0%

Model Accuracy

0.0M+

People Protected

24/7

Climate Monitoring

GPU Cluster:12× A100(94% util)Models Training:3 active(Epoch 847)Inferences/sec:142(Live)Dataset Size:14.2 TB(Growing)GPU Cluster:12× A100(94% util)Models Training:3 active(Epoch 847)Inferences/sec:142(Live)Dataset Size:14.2 TB(Growing)
ML Infrastructure

Built for AI Training at Scale

End-to-end geospatial ML pipeline — from satellite ingestion to GPU-accelerated model training and real-time climate inference across Africa.

Satellite Ingest

Sentinel-2 · Landsat-9 · SAR

Data Pipeline

GeoTIFF · NetCDF · GeoJSON

GPU Training

A100 · 40GB VRAM · PyTorch

Model Ensemble

Drought · Flood · NDVI nets

Inference API

15K+ predictions/day

0

GPU Hours / Month

0.0M

Model Parameters

0.0%

Avg. Accuracy

boldcomp-train — gpu-node-03

Training Loss — DroughtRiskNet

Epoch 847 · converging

Train Loss0.0234
Val Accuracy89.1%
GPU Memory38.2 GB

Platform Impact

Real-time metrics from our climate intelligence network

Counties Monitored

47

+12

Community Reports

2,840

+340

AI Predictions/Day

15K+

+22%

Climate Projects Tracked

$10.8M

+$2.1M

Regional Risk Trends — East Africa

Environmental Impact Metrics

Our AI models continuously assess ecosystem health, water security, carbon sequestration, and air quality across monitored regions — providing grant-ready impact data for climate finance and adaptation programs.

Satellite VerifiedCommunity ValidatedAI Ensemble Models

72/100

Ecosystem Health Score

+4 pts

58/100

Water Security Index

-6 pts

1.2M tCO₂e

Carbon Sequestration

+8%

Good

Air Quality Index

Stable

12× A100 GPUs available for climate model training

Ready to Train Climate AI at Scale?

Join accelerators and researchers leveraging BoldComp Africa's geospatial ML infrastructure — from dataset pipelines to GPU training and deployment.