1 GPU on the selected test

granite-speech-5.0-470m-turboctc

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Test conditions

Every GPU below ran exactly these conditions.

Task: Speech recognition · Audio → Text
Workload: Audio → Text · asr_prompts · Test duration 16 s
Batch size: Batch 256
Format · Engine: Native · transformers-ctc · PyTorch 2.13.0
Unit: audio-sec/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

audio-native-v1 · asr_prompts · asr

GPUs on this test

Only one GPU configuration has matching measurements; a cross-hardware comparison is not yet available.

RTX PRO 6000 Blackwell Server Edition · 96 GBPyTorch 2.13.0
Mean throughput
8,343audio-sec/sec
Observed GPU occupancy
20.1GiB
P95 latency
Not recorded
Reports combined
1
Runs, parameters and setup ↗

Sortable table

Hardware ranking · 1 rows
Hardware	Mean throughput (audio-sec/sec)	P95 (s)	TTFT (ms)	Observed occupancy (GiB)	GPU power (W)	Throughput / W	VRAM per GPU (GB)	Reports combined
#1RTX PRO 6000 Blackwell Server Edition · 96 GBPyTorch 2.13.0Runs, parameters and setup ↗	8,343	—	—	20.09	477.57	17.47	96	1

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, 11:05:29 PM