VLM Inference Benchmark Explorer
MedGemma-1.5-4B-it on RTX PRO 6000 — BF16, vLLM vision-language inference benchmark
Open this configuration in the VLM Inference Benchmark Explorer
| Model | MedGemma-1.5-4B-it |
|---|---|
| Parameters | 4.3B |
| Device | RTX PRO 6000 |
| Quantization | BF16 |
| Image size | 720p |
| Images per camera | 1 |
| Max Cameras (response ≤ 3 s) | 16+ |
| Engine | vLLM |
| Cameras | Response (s) | TPS (tok/s) | Status |
|---|---|---|---|
| 1 | 0.20 | 171.5 | PASS |
| 2 | 0.16 | 165.1 | PASS |
| 4 | 0.16 | 154.0 | PASS |
| 8 | 0.22 | 143.2 | PASS |
| 16 | 0.40 | 134.1 | PASS |
MedGemma-1.5-4B-it (4.3B parameters), served in BF16 format with vLLM on RTX PRO 6000, starts answering a single camera that sends one 720p image after 0.2 s, then writes 171.5 tok/s.
With 5 images per camera at 720p, the answer starts after 0.45 s. With one 2K image, after 0.25 s.
At the default target of an answer starting within 3 s (720p, one image per camera), it keeps up with at least 16 cameras at once: it still met the target at the highest number measured, so the real limit is higher.
MedGemma-1.5-4B-it (4.3B parameters), served in BF16 format with vLLM on RTX PRO 6000, starts answering a single camera that sends one 720p image after 0.2 s, then writes 171.5 tok/s.
With 5 images per camera at 720p, the answer starts after 0.45 s. With one 2K image, after 0.25 s.
At the default target of an answer starting within 3 s (720p, one image per camera), it keeps up with at least 16 cameras at once: it still met the target at the highest number measured, so the real limit is higher.