# GPU benchmark batch_size/$ fidelity

## post by Igor on Nov 12, 2022

Does somebody know why A4000 and A6000 has ~35% higher `batch_size/$` ratio vs. 3090? Also why 3090 and A4500 have only marginal difference?

Assuming `$` here is a price per GPU at launch – A4500 and 3090 used to cost ~2200$ and ~1700$ respectively. Both have the same 24GB of VRAM. From this, I would expect 3090’s `batch_size/$` to be ~30% greater than A4500. In PyTorch benchmark, 3090 and A4500 has almost the same `batch_size/$`.

For A6000, with the price around 4500$ and 48GB of VRAM, 3090 is still expected to be 30% more efficient in `batch_size/$`, but it’s the opposite in the chart.

3090 vs A4000, A4000 is ~25% higher, thought, with A4000 price being around 1250$ and VRAM of 16GB, I would still expect 3090 to have ~10% higher `batch_size/$`.

Regarding the price `$` here, it is not clear, but I assume `$` corresponds to the board and not the whole server.

Lambda has one of greatest GPU benchmarks, though I cannot find methodology on how this and some other criteria are computed.