About GPUCalc
Built for AI engineers, ML researchers, and infrastructure architects who need deterministic VRAM calculations and transparent cloud GPU pricing before spinning up expensive clusters.
Deterministic Formulas
We model PyTorch CUDA allocator behavior, including KV-cache expansion, GQA ratios, LoRA trainable weights, AdamW optimizer momentums, and runtime fragmentation headroom.
Multi-Cloud Pricing
Aggregating verified spot and on-demand rates across RunPod, Lambda Labs, Vast.ai, AWS EC2, and GCP to ensure you never overpay for GPU compute.
100% Client-Side Privacy
All computations, configurations, and slider interactions execute strictly in your browser. No model weights, custom architectures, or telemetry data are collected.
Supported Hardware & Architectures
Next-generation 192GB HBM3e and 32GB GDDR7 architecture with 2nd-gen Transformer Engine and native FP4/FP8 compute acceleration.
The enterprise training and serving flagship with 80GB to 141GB HBM3 memory, NVLink-4 (900 GB/s), and FP8 Transformer Engine support.
The consumer and workstation champion with 24GB to 48GB GDDR6X, high memory bandwidth (1008 GB/s), and 4th-gen Tensor Cores.
Battle-tested high-VRAM data center standards supporting PCIe 4.0, NVLink-3, and TensorFloat-32 (TF32) precision.
Ready to Size Your Next Model Deployment?
Switch between presets, configure context length, test QLoRA adapters, and find the lowest cost cloud GPU in seconds.