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Embedding · RTX 4080 SUPER · 32 GB

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Qwen3-Embedding-0.6B

Qwen · 2 tested configurations · 2 GPUs

RTX 4090 · 24 GBfp16 · Text → Vectors · embedding-mteb-tasks-v1 · Test duration 90 s

Throughput · Batch 1: 259vectors/sec
Best measured · Batch 16: 2,018vectors/sec
Observed GPU occupancy: 1.8GiB
P95 latency: 4 ms

Use this recipe · Batch 16 ↗Evidence report and run availability ↗Same test on other GPUs

Test details · 3 batch sizes

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.

Engine: sentence-transformers · PyTorch 2.12.1+cu126
Protocol: native-forward-v1
Dataset: embedding-mteb-tasks-v1
Workload: embedding

Batch size	Mean throughput (vectors/sec)	P95	TTFT	Observed occupancy (GiB)	GPU power (W)	Reports combined	View report
B1	258.52	4 ms	—	1.84	151.6	1	3ba3d2cb ↗
B4	921.44	4 ms	—	1.84	159.5	1	3ba3d2cb ↗
B16	2,017.74	8 ms	—	1.84	133	1	3ba3d2cb ↗

Show 1 more GPUs

V100-PCIE-32GB · 32 GBfp16 · Text → Vectors · embedding-mteb-tasks-v1 · Test duration 90 s

Throughput · Batch 1: 95.3vectors/sec
Best measured · Batch 16: 771vectors/sec
Observed GPU occupancy: 1.7GiB
P95 latency: 11 ms

Use this recipe · Batch 16 ↗Evidence report and run availability ↗Same test on other GPUs

Test details · 3 batch sizes

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.

Engine: sentence-transformers · PyTorch 2.12.1+cu126
Protocol: native-forward-v1
Dataset: embedding-mteb-tasks-v1
Workload: embedding

Batch size	Mean throughput (vectors/sec)	P95	TTFT	Observed occupancy (GiB)	GPU power (W)	Reports combined	View report
B1	95.33	11 ms	—	1.75	70.8	1	f91802d1 ↗
B4	308.67	13 ms	—	1.75	90.1	1	f91802d1 ↗
B16	771.26	21 ms	—	1.75	99.2	1	f91802d1 ↗