About Flopper.io
Flopper.io is the searchable database for AI GPU performance. We collect, normalize, and present specifications from NVIDIA, AMD, and Intel accelerators — everything from FLOPS at every precision (FP64, FP32, FP16, BF16, FP8, TF32) to power efficiency, memory bandwidth, and system-level configurations like DGX and HGX.
Our goal is simple: make it easy to understand and compare GPU performance for AI training and inference without digging through dozens of PDFs and marketing slides.
Why We Built It
- 🚀 The landscape is confusing. Specs are scattered across datasheets, press releases, and benchmarks.
- ⚡ Performance means more than FLOPS. Power, efficiency, and memory bandwidth matter just as much.
- 📊 Comparisons should be simple. Engineers and researchers shouldn't waste hours collecting numbers.
Flopper exists to bring clarity to the AI GPU world, just like instances.vantage.sh did for cloud VMs.
What You'll Find Here
FLOPS, TDP, memory, bandwidth, efficiency.
DGX, HGX, OAM, and multi-GPU setups.
Side-by-side performance across vendors and generations.
Blog posts explaining FLOPS, precision formats, perf/W, and more.
Who This Is For
Flopper.io is built for:
Understanding GPU Metrics
What is FLOPS?
Floating-point operations per second. This measures the computational throughput of a GPU for different numerical precision formats. Higher FLOPS = more calculations per second.
Precision Formats Explained
FP64: Double precision (rarely used in AI)
FP32: Standard precision for AI model training
FP16/BF16: Half precision for faster mixed precision training
FP8: Ultra-efficient format for inference and large models
Memory Bandwidth
Measured in GB/s, this indicates how fast data can move between the GPU's compute cores and memory. Critical for training large language models and serving high-throughput inference.
TDP & Power Efficiency
Thermal Design Power (TDP) in watts indicates power consumption. Compare FLOPS/W to find the most efficient GPU for your AI infrastructure and datacenter power budget.
Our Approach
We rely on:
- 🔍 Official datasheets from NVIDIA, AMD, and Intel.
- 📑 Benchmark sources like MLPerf and LINPACK.
- ⚙️ Calculated aggregates for system-level numbers (e.g., 8× H200 = 32 PFLOPS).
Stay in the Loop
We're constantly updating the database as new GPUs are announced.