Benchmarks

NVIDIA RTX A4500 BERT Large Fine Tuning Benchmarks in TensorFlow

February 17, 2022

13 min read

Fine-tuning BERT Large on a GPU Workstation

For this post, we measured fine-tuning performance (training and inference) for the BERT implementation of TensorFlow on NVIDIA RTX A4500 GPUs. For testing we used an Exxact Valence Workstation fitted with 8x RTX A4500 GPUs with 20GB GPU memory per GPU.

Benchmark scripts we used for evaluation were the finetune_train_benchmark.sh and finetune_inference_benchmark.sh from NVIDIA NGC Repository BERT for TensorFlow. We made slight modifications to the training benchmark script to get the larger batch size numbers.

The script runs multiple tests on the SQuAD v1.1 dataset using batch sizes 1, 2, 4, and 8. Inferencing tests were conducted using a 1x GPU configurations on BERT Large. In addition, we ran all benchmarks using TensorFlow's XLA across the board.

Key Points and Observations

Scenarios that are not typically used in real-world training, such as single GPU throughput are illustrated in the table below, and provided for reference as an indication of single chip throughput of the platform.
For those interested in training BERT Large, a 2x RTX A4500 system may be a great choice to start with, giving the opportunity to add additional cards as budget/scaling needs increase.
NOTE: In order to run these benchmarks, or be able to fine-tune BERT Large with 4x GPUs you'll need a system with at least 64GB RAM.

Interested in getting faster results?
Learn more about Exxact AI workstations to do NLP training on starting around $5,500

Exxact Workstation System Specs:

Nodes	1
Processor / Count	2x AMD EPYC 7552
Total Logical Cores	48
Memory	DDR4 512GB
Storage	NVMe 3.84TB
OS	Ubuntu 18.04
CUDA Version	11.4
BERT Dataset	squad v1
TensorFlow	2.40

GPU Benchmark Overview

FP = Floating Point Precision, Seq = Sequence Length, BS = Batch Size

1x Quadro RTX A4500 BERT LARGE Inference Benchmark

Raw Data

Model	Sequence-Length	Batch-size	Precision	Total-Inference-Time	Throughput-Average(sent/sec)	Latency-Average(ms)	Latency-50%(ms)	Latency-90%(ms)	Latency-95%(ms)	iLatency-99%(ms)	Latency-100%(ms)
base	128	1	fp16	25.98	186.13	8.94	5.56	6.34	6.46	6.8	6387.51
base	128	1	fp32	20.34	188.55	8.38	5.48	6.32	6.48	6.85	5501.44
base	128	2	fp16	20.47	364.38	8.84	5.67	6.2	6.32	6.78	5679.04
base	128	2	fp32	19.65	389.55	8.47	5.3	5.89	5.99	6.18	5654.98
base	128	4	fp16	26.39	695.64	11.55	5.79	6.24	6.36	6.9	5774.98
base	128	4	fp32	26.41	696.75	11.54	5.76	6.22	6.41	6.86	5767.47
base	128	8	fp16	34.2	964.18	13.57	8.27	8.72	8.94	9.52	5876.52
base	128	8	fp32	34.42	965.47	13.6	8.28	8.64	8.89	9.44	5899.06
base	384	1	fp16	17.03	177.1	11.46	5.65	6.11	6.25	6.7	6059.77
base	384	1	fp32	17.2	176.11	11.49	5.66	6.15	6.29	6.77	6051.43
base	384	2	fp16	25.64	239.89	19.3	8.31	8.84	9.13	9.84	6456.22
base	384	2	fp32	25.6	240.18	19.26	8.29	8.8	9.06	9.77	6447.57
base	384	4	fp16	31.28	305.32	24.29	13.09	13.62	13.85	14.97	6660.95
base	384	4	fp32	31.11	304.43	24.28	13.13	13.6	13.8	14.98	6639.34
base	384	8	fp16	43.89	358.82	34.29	22.27	22.79	23.1	24.51	7002.83
base	384	8	fp32	43.9	358.76	34.33	22.26	22.8	23.11	24.35	7031.13
large	128	1	fp16	47.42	103.62	15.96	9.42	10.95	11.16	11.67	11340.21
large	128	1	fp32	36.28	108.7	15.52	8.91	10.12	10.4	10.96	11350.15
large	128	2	fp16	38.82	180.4	17.77	11.09	11.91	12.05	12.44	11356.99
large	128	2	fp32	39.12	177.62	17.97	11.45	11.98	12.13	12.79	11389.43
large	128	4	fp16	58.85	253.71	27.84	15.94	16.48	16.65	17.52	11607
large	128	4	fp32	57.8	261.94	27.35	15.01	16.17	16.35	17.01	11606.73
large	128	8	fp16	78.49	348.55	34.15	22.89	23.82	24.05	24.71	11839.75
large	128	8	fp32	79.35	345.57	34.53	23.22	23.96	24.19	24.88	12047.05
large	384	1	fp16	34.49	77.09	24.99	12.98	13.76	13.89	14.59	12534.02
large	384	1	fp32	33.95	79.81	24.49	12.34	13.35	13.51	14.09	12472.18
large	384	2	fp16	54.4	97.77	44.04	20.34	21.21	21.38	22.2	13302.25
large	384	2	fp32	53.88	98.22	43.83	20.12	21.18	21.38	22.19	13331.07
large	384	4	fp16	68.97	118.22	57.65	33.95	34.44	34.62	35.31	13713.5
large	384	4	fp32	69.14	117.88	57.75	34.07	34.59	34.78	35.73	13693.18
large	384	8	fp16	104.65	126.65	88.76	63.22	63.87	64.17	65.32	14498.6
large	384	8	fp32	104.84	126.41	89	63.33	64.11	64.4	65.53	14539.54

Data Chart

Finetune Inferencing BERT for TensorFlow 2

Data Chart

8x RTX A4500 Benchmarks BERT for TensorFlow 2 FineTuning Training

Raw Data

	Training Time Hours	Throughput sentences/sec
FP16, Seq 128, BS1	2247.97	87.04
FP32, Seq 128, BS1	1859.29	107.77
FP16, Seq 128, BS2	996.29	228.01
FP32, Seq 128, BS2	994.02	228.55
FP16, Seq 128, BS4	617.1	449.09
FP32, Seq 128, BS4	618.85	449.5
FP16, Seq 128, BS8	392.02	807.31
FP32, Seq 128, BS8	391.99	809.54
FP16, Seq 384, BS1	1778.13	113.63
FP32, Seq 384, BS1	1781.29	113.46
FP16, Seq 384, BS2	1086.58	205.96
FP32, Seq 384, BS2	1081.24	207.12
FP16, Seq 384, BS4	742.6	346.99
FP32, Seq 384, BS4	743.12	346.3
FP16, Seq 384, BS8	520.45	521.61
FP32, Seq 384, BS8	519.85	522.77

Data Chart

Finetune Inferencing BERT for TensorFlow 2 Chart 2

NVIDIA RTX A4500 Series GPUs

GPU Features	NVIDIA RTX A4500
GPU Memory	20GB GDDR6 with error-correction code (ECC)
Display Ports	4x DisplayPort 1.4
Max Power Consumption	2000 W
Graphics Bus	PCI Express Gen 4 x 16
Form Factor	4.4” (H) x 10.5” (L) Dual Slot
Thermal	Active
NVLink	2-way Low-profile (2-slot and 3-slot bridges)
VR Ready	Yes

Additional GPU Benchmarks

Exxact Workstation System Specs

Nodes	1
Processor / Count	2x AMD EPYC 7552
Total Logical Cores	48
Memory	DDR4 512GB
Storage	NVMe 3.84TB
OS	Ubuntu 18.04
CUDA Version	11.2
BERT Dataset	squad v1

Additional GPU Benchmarks

Have any questions?
Contact Exxact Today

Topics

bert

transformers

nlp

tensorflow

benchmarks

a4500

natural language processing

nvidia

Have any questions?

Benchmarks