T5 Results

You can also prompt-learn on top of any .nemo trained checkpoint file on the SQuAD task mentioned in the section T5 and mT5 Prompt Learning. The results are shown in the table below.

Training the 220M T5 model to convergence takes 4 days, and the loss curve is shown in the figure below:

220M T5 Training Loss

The table below shows the converged training loss, the throughput, and the total time to train for the 220M T5 model, using a given number of GPUs and a given Global Batch Size (GBS).

Training the 3B T5 model to convergence takes 11 days, and the loss curve of a fully trained model can be seen in the figure below:

3B T5 Training Loss

The table below shows the converged training loss, the throughput, and the total time to train for the 3B T5 model, using a given number of GPUs and a given Global Batch Size (GBS).

Training performance: NVIDIA DGX SuperPOD (20 × 8 × A100 80GB for 3B T5 Model)

NVIDIA measured the throughput of training a 3B parameter T5 model on NVIDIA DGX SuperPOD using different numbers of nodes. Scaling from 1 node to 20 nodes yielded a 16.38× speed-up.

NVIDIA is actively working on improving the scaling performance for T5 models. The table and chart below show the performance results.

3B T5 NeMo Framework Throughput

Inference performance was measured for NVIDIA DGX SuperPOD (1 × 8 × A100 80GB). The results are shown in the table below.

Inference configurations:

• Batch size: 1

• Input tokens length: 60

• Output tokens length: 20

Average Latency vs T5 Model Size