GPU Benchmarks for Real AI Workloads
This page shows how GPUCoreHost measures actual AI performance across different GPU hosting providers using standardized, reproducible workloads.
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What These Benchmarks Measure
- Training speed
- Multi-GPU scaling
- Cost per job
- Time to results
- Stability
Standardized Benchmark Setup
Every test follows the same structure: identical model, dataset, scripts, and multiple runs for consistency.
Core Metrics Explained
Training Throughput
Measured as tokens/sec, samples/sec or images/sec depending on workload.
Time-to-First-GPU
The time required to provision and start training on a new instance.
Multi-GPU Scaling
Measures efficiency across multiple GPUs and network bottlenecks.
Cost Efficiency
Cost per completed workload rather than hourly rate.
Benchmark Workloads
| Workload Type | Example Use |
|---|---|
| LLM Fine-Tuning | LLaMA / Mistral |
| Image Generation | Stable Diffusion |
| Computer Vision | ResNet / YOLO |
| Inference | Real-time API serving |
Example Results Table
| Provider | GPU | Throughput | Runtime | Total Cost |
|---|---|---|---|---|
| Provider A | A100 | 12k tokens/s | 2.1 hrs | $7.20 |
| Provider B | A100 | 10k tokens/s | 2.5 hrs | $6.80 |
| Provider C | A6000 | 6k tokens/s | 4.1 hrs | $9.50 |
Multi-GPU Scaling Example
| GPUs | Ideal Scaling | Actual Scaling |
|---|---|---|
| 1 | 1x | 1x |
| 2 | 2x | 1.85x |
| 4 | 4x | 3.20x |
| 8 | 8x | 5.90x |
How to Interpret Benchmarks
Use cost per job over hourly price, measure full workflow, include setup time, and consider reliability.
Common Benchmark Pitfalls
- Relying on synthetic benchmarks
- Ignoring setup time
- Comparing hourly price only
- Vendor-provided tests only
