Introduction
VisionAI develops real‑time object detection for retail. They needed affordable GPU compute for training. This case study covers their experience with Hostxpeed’s GPU VPS (Nvidia A100).
The Problem
Training on CPU was impossible (60 days). Cloud GPU from AWS (p3.2xlarge) cost $3.06/hour → $73/day. A 3‑day training run cost $220. Monthly training budget exceeded $6,000.
Hostxpeed GPU Solution
Hostxpeed GPU‑1: 8 vCPU, 32GB RAM, 1x Nvidia A100 (40GB), 500GB NVMe, $0.99/hour ($23.76/day). Training time reduced from 3 days to 8 hours (same model). Cost per training run: $7.92 (vs $220). Monthly training budget down to $500 (92% reduction).
Performance Metrics
A100 delivered 312 TFLOPS (FP16) – 20x faster than CPU. CUDA 12.2 and cuDNN 9 pre‑installed. Inter‑GPU NVLink for multi‑GPU scaling (used 4x A100 for large model).
Workflow
Used Docker containers with TensorFlow. Data stored on Hostxpeed object storage (S3‑compatible). Training jobs scheduled via API. Spot instances available at $0.49/hour (54% off).
Conclusion
VisionAI now runs continuous training on Hostxpeed GPU VPS, iterating daily instead of weekly. They reduced infrastructure cost by 92% and accelerated time‑to‑market.