Important

NeMo 2.0 is an experimental feature and currently released in the dev container only: nvcr.io/nvidia/nemo:dev. Please refer to the Migration Guide for information on getting started.

Performance

The NVIDIA NeMo Framework accelerates the entire AI workflow end-to-end, from data preparation to model training to inference. It provides optimal performance for training advanced generative AI models by incorporating the most recent training techniques, such as model parallelization, optimized attention mechanisms, and more, to achieve high training throughput. For inference, the NeMo Framework provides a path that leverages TensorRT-LLM, a specialized library for accelerating and optimizing LLM inference on NVIDIA GPUs.

Below, you can see performance benchmarks for various large language models.

Performance Summary for Large Language Models

Pretraining

The table below shows the pre-training performance of various models at FP8 precision.

Model

#-GPUs

GBS

MBS

Sequence Length

TP

PP

CP

VP

Tokens / sec / GPU

Model TFLOP / sec / GPU

Est. time to train in days (10T tokens, 1K GPUs)

GPT3-5B

64

2048

4

2048

1

1

1

1

23574

770

5

GPT3-20B

64

256

2

2048

2

1

1

1

5894

755

19

GPT3-175B

128

256

1

2048

4

8

1

6

745

802

152

GPT3-175B

512

2048

2

2048

4

8

1

6

832

895

136

LLAMA2-7B

8

128

1

4096

1

1

1

1

16634

767

7

LLAMA2-13B

16

128

1

4096

1

4

1

10

8715

760

13

LLAMA2-70B

64

128

1

4096

4

4

1

20

1717

763

66

Nemotron-8B

64

256

4

4096

2

1

1

1

12507

643

9

Nemotron-22B

64

256

2

4096

2

4

1

10

4289

559

26

Nemotron-340B

128

32

1

4096

8

8

1

12

328

691

344

LLAMA3-8B

8

128

1

8192

1

1

2

1

11883

688

10

LLAMA3-70B

64

128

1

8192

4

4

2

5

1549

746

73

Fine-Tuning

The table below presents the fine-tuning performance of LLaMA2 models using Supervised Fine-Tuning (SFT) and Low-Rank Adaptors (LoRA) at FP8 precision.

For fine-tuning, we use the SQuAD-v1.1 dataset, with inputs packed to 4096 tokens.

Model

Task

#-GPUs

GBS

MBS

Packed Sequence Length

TP

PP

Tokens / sec / GPU

Model TFLOP / sec / GPU

Est. time to complete in mins (10M tokens)

LLAMA2-7B

SFT

8

32

1

4096

1

1

17617

702

1.2

LLAMA2-13B

SFT

8

32

1

4096

1

4

10176

787

2.0

LLAMA2-70B

SFT

16

32

1

4096

4

4

1812

747

5.7

LLAMA2-7B

LoRA

8

32

1

4096

1

1

25206

673

0.8

LLAMA2-13B

LoRA

8

32

1

4096

1

1

14760

764

1.4

LLAMA2-70B

LoRA

8

32

1

4096

2

4

2621

722

7.9

Example Scripts for Pretraining and Fine-tuning

These scripts run a recommended configuration for GPT-3, LLaMA-2, NeMo pretraining, and fine-tuning for various model sizes on A100 and H100. For example, for GPT-3 pretraining, the following folders provide sample scripts:

  • A100 : Scripts to run GPT pretraining on NVIDIA A100, in bf16 data type.

  • H100 : Scripts to run GPT pretraining for NVIDIA H100, in fp8 data type.

Set Up Example Scripts

  1. To run these scripts, you must have access to the NeMo Framework Container. Please sign in at NGC (user = ea-bignlp/ga-participants) to access the catalog.

  2. Update the following bash variables in the example run scripts:

    • NEMO_MEGATRON_LAUNCHER_DIR: the directory of where this repository is located.

    • DATA_DIR : the directory of the dataset used for pretraining, by default this is NEMO_MEGATRON_LAUNCHER_DIR/data.

  3. Enter your cluster environment settings at config.yaml.

    For bcm type clusters update the job name, partition, and account at bcm.yaml.

  4. For testing performance with synthetic data on an interactive node, you need to add the following options to your bash script:

    cluster_type=interactive \
++training.cluster_type=BCP \
training.model.data.data_impl="mock" \
training.model.data.data_prefix=[]

Collect Results

For performance, the “step_time_per_sec” variable on the console out provides a quick way to read performance of a workload.

For more details and graphics, you can use TensorBoard or Weights and Biases. To do so, use the results stored in NEMO_MEGATRON_LAUNCHER_DIR/results/<experiment_name> with the following structure:

  • NEMO_MEGATRON_LAUNCHER_DIR/results/<experiment_name>/<experiment_name>.yaml : The config of the pretrained model.

  • NEMO_MEGATRON_LAUNCHER_DIR/results/<experiment_name>/<jobname>_<experiment_name>.sh : The autogenerated .sh file that was run.

  • NEMO_MEGATRON_LAUNCHER_DIR/results/<experiment_name>/results/ : Directory contained per rank logs, and tensorboard data.