embeddinggemma-2
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Test conditions
- Task
- Embedding · Text → Vectors
- Workload
- Text → Vectors · embedding-sanity-v1
- Batch size
- Batch 4
- Format · Engine
- bf16 · sentence-transformers
- Unit
- vectors/sec
Occupancy includes engine buffers and reserved KV cache. It is not the minimum VRAM required by the model.
Reports combined counts reports, not inference requests. Request successes and failures are listed in each report.
Protocol details
native-forward-v1 · embedding-sanity-v1 · embedding
GPUs on this test
Only one GPU configuration has matching measurements; a cross-hardware comparison is not yet available.
- Mean throughput
- 120vectors/sec
- Observed GPU occupancy
- Not recorded
- P95 latency
- 44 ms
- Reports combined
- 1
3 more GPUs ran this model under other tests
Sortable table
| Hardware | Mean throughput (vectors/sec) | P95 (s) | TTFT (ms) | Observed occupancy (GiB) | GPU power (W) | Throughput / W | VRAM per GPU (GB) | Reports combined |
|---|---|---|---|---|---|---|---|---|
| #1vGPU-32GB · 32 GBRuns, parameters and setup ↗ | 119.85 | 0.044 | — | — | — | — | 32 | 1 |
| L4 · 24 GBNo matching measurements | — | — | — | — | — | — | 24 | — |
| RTX PRO 6000 Blackwell Server Edition · 96 GBNo matching measurements | — | — | — | — | — | — | 96 | — |
| T4 · 16 GBNo matching measurements | — | — | — | — | — | — | 16 | — |
Occupancy includes engine buffers and reserved KV cache. It is not the minimum VRAM required by the model.
Reports combined counts reports, not inference requests. Request successes and failures are listed in each report.
How results are grouped
Results merge successful runs of the same model, precision and workload. CPU thread counts and CUDA wheel builds may differ; actual PyTorch builds are shown. Other recorded protocol settings remain separate.
Updated 11/5/2026, 11:02:27 PM