Performance#
As part of the NVIDIA NeMo Framework, Megatron Bridge, 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.
This page provides performance benchmarks for large language models using Megatron-Bridge across different GPU systems and configurations.
Nomenclature#
GBS: Global Batch Size
MBS: Micro Batch Size
FSDP: Fully Sharded Data Parallel
FSDP = 1: use FSDP
FSDP = 0: use DDP (Distributed Data Parallel)
TP: Tensor Parallel Size
PP: Pipeline Parallel Size
CP: Context Parallel Size
VP: Virtual Pipeline Parallel Size
EP: Expert Parallel Size
GA: Number of Gradient Accumulations
Performance Metrics#
Performance is measured using:
Tokens/sec/GPU: Throughput per GPU
Model TFLOP/sec/GPU: Model floating-point operations per second per GPU
Performance Summary for Large Language Models#
Below are performance benchmarks for various large language models organized by release version. These results were obtained using performance recipes available here.
The performance data includes:
Pre-training Performance: Throughput metrics for various model sizes and architectures
System Configurations: Results across different GPU systems (DGX-GB200, DGX-B200, DGX-H100)
Precision Options: Performance comparisons between different precision modes (BF16, FP8, MXFP8)
25.09 NeMo Container#
Pre-Training Performance#
System: DGX-GB200#
Model | #-GPUs | GBS | MBS | Sequence Length | FSDP | TP | PP | CP | VP | EP | GA | Tokens / sec / GPU | Model TFLOP / sec / GPU |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LLAMA3_8B | 8 | 128 | 2 | 8192 | 0 | 1 | 1 | 1 | n/a | 1 | 8 | 31357 (29925) | 1614 (1540) |
LLAMA3_70B | 64 | 128 | 2 | 8192 | 1 (0) | 1 (2) | 1 (4) | 1 | 1 (5) | 1 | 1 (16) | 3986 (3546) | 1791 (1593) |
LLAMA3.1_405B | 128 | 64 | 1 | 8192 | 1 (0) | 2 (4) | 1 (8) | 1 (2) | 1 (8) | 1 | 1 (32) | 729 (578) | 1840 (1458) |
DeepSeekV3 (tokendrop) | 256 | 2048 | 1 | 4096 | 0 | 1 | 4 (8) | 1 | 4 (2) | 64 | 32 (64) | 3454 (2835) | 899 (738) |
Qwen3_30B_a3B (tokendrop) | 8 | 512 | 4 | 4096 | 0 | 1 | 1 | 1 | 1 | 8 | 16 | 22775 (23723) | 524 (546) |
Qwen3_235B_a22B (tokendrop) | 64 | 1024 | 1 | 4096 | 0 | 2 | 1 | 1 | 1 | 64 | 32 | 4452 (4416) | 659 (654) |
System: DGX-B200#
Model | #-GPUs | GBS | MBS | Sequence Length | FSDP | TP | PP | CP | VP | EP | GA | Tokens / sec / GPU | Model TFLOP / sec / GPU |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LLAMA3_8B | 8 | 128 | 2 | 8192 | 0 | 1 | 1 | 1 | n/a | 1 | 8 | 29994 (29388) | 1544 (1513) |
LLAMA3.1_405B | 128 | 64 | 1 | 8192 | 0 | 4 | 8 | 2 | 8 | 1 | 32 | 664 (622) | 1676 (1569) |
DeepSeekV3 (tokendrop) | 256 | 2048 | 1 | 4096 | 0 | 1 | 16 | 1 | 1 | 8 | 128 | 2265 (2159) | 589 (562) |
Qwen3_30B_a3B (tokendrop) | 8 | 512 | 1 | 4096 | 0 | 1 | 1 | 1 | 1 | 8 | 64 | 18066 | 416 |
Qwen3_235B_a22B (tokendrop) | 64 | 1024 | 1 | 4096 | 0 | 1 | 8 | 1 | 2 | 8 | 128 | 4104 (4275) | 607 (633) |
System: DGX-H100#
Model | #-GPUs | GBS | MBS | Sequence Length | FSDP | TP | PP | CP | VP | EP | GA | Tokens / sec / GPU | Model TFLOP / sec / GPU |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LLAMA3_8B | 8 | 128 | 1 | 8192 | 1 | 1 | 1 | 1 | n/a | 1 | 16 | 14079 | 725 |
LLAMA3_70B | 64 | 128 | 1 | 8192 | 0 | 4 | 8 | 1 | 5 | 1 | 64 | 1619 | 727 |
LLAMA3.1_405B | 1024 | 512 | 1 | 8192 | 0 | 8 | 8 | 2 | 8 | 1 | 64 | 302 | 763 |
DeepSeekV3 (dropless) | 1024 | 8192 | 1 | 4096 | 0 | 2 | 8 | 1 | 4 | 64 | 128 | 1297 | 338 (330) |
Qwen3_30B_a3B (tokendrop) | 16 | 512 | 2 | 4096 | 0 | 1 | 2 | 1 | 24 | 8 | 32 | 10494 | 241 |
Qwen3_235B_a22B (tokendrop) | 256 | 2048 | 1 | 4096 | 0 | 2 | 8 | 1 | 4 | 32 | 128 | 1204 | 178 |
The numbers in parentheses indicate the use of different quantization granularities: In case of Gb200 and B200 systems, 32×32 for both weights and activations. For H100 system, 128×128 for weights and 1×128 for activations, which match those used in the original DeepSeekV3 pre-training.
In token-dropless MoE trianing benchmarks, we force-balance the token distribution among experts.