VRAM Calculator: Find the Right GPU for Any HuggingFace Model

The estimate lands within about 15% of real-world memory use for most transformer models. It covers model weights, activations, KV cache, and overhead. Real numbers shift with your inference engine, batch size, and context window, so leave 10% headroom for production traffic.

At FP16, a 70B model needs roughly 140 GB of VRAM for inference, which means 2x H100 80GB or a single H200 141GB. Quantized to INT4 it drops to around 40 GB, which fits on a single H100 or RTX PRO 6000. Full fine-tuning quadruples those numbers because the GPU also holds gradients and optimizer state.

Yes, with the right setup. A 70B model in INT4 or INT8 quantization fits on a single H100 80GB, H200, or RTX PRO 6000 96GB with room for moderate batch sizes. For FP16 you need at least 2 GPUs split across tensor parallel or a single 141GB+ card. The tool shows both options ranked by cost.

For LoRA fine-tuning a 7B model, an RTX 4090 or L40S does the job for under $1/hr. For QLoRA on a 70B model, a single H100 is the right pick at around $1.33/hr. Full-parameter fine-tuning needs 4x to 8x the memory of inference, which usually puts you in H100 or B200 territory.

Gated and private models return a 401 from the HuggingFace API, so the tool cannot read their parameter count automatically. Request access on the HuggingFace model page first. If you already know the parameter count, enter it in advanced settings and the tool will pick a GPU from there.

Training holds three extras the GPU does not need at inference time: gradients, optimizer state, and activation memory for backprop. The rough multiplier is 4x for full fine-tuning and around 1.5x for LoRA. A 13B model that fits in 26 GB for inference needs 100+ GB to train from scratch.

Spheron aggregates GPU supply from 5+ data center partners and routes you to the cheapest one. For H100, that runs about 50% to 70% below AWS p5 and 30% to 40% below Lambda on-demand. Billing is per-minute with no minimum spend and no contracts.