GPU VRAM Calculator for LLMs
Estimate the VRAM needed to run a model locally.
Results update automatically as you type.
Result
~168 GB VRAM
≈ 3 x 80 GB GPU(s) to serve
- Parameters
- 70 B
- Precision
- fp16
- Weights + overhead
- 168 GB
- GPUs needed
- 3
Estimate how much GPU memory a model needs based on its parameter count and precision, and how many GPUs it takes to serve — essential before running models locally.
How this is calculated
This calculator uses the infrastructure formulas described in our methodology. All figures are estimates for planning; verify current pricing with each provider before relying on them.
Worked example (defaults)
With the default inputs above, here is the result:
Result
~168 GB VRAM
≈ 3 x 80 GB GPU(s) to serve
- Parameters
- 70 B
- Precision
- fp16
- Weights + overhead
- 168 GB
- GPUs needed
- 3
Frequently asked questions
How much VRAM does a 70B model need?+
About 168 GB at float16 (2 bytes/param + overhead), so 2–3 high-end 80 GB GPUs. int4 quantization drops it to ~42 GB.
What's the overhead for?+
KV cache, activations and framework overhead on top of the raw weights, roughly 15–30%.