Key Features
LLM Training & Inference
Train and deploy large language models with parallelism and memory optimization.
Conversational AI
Build chatbots and virtual assistants with intent detection and response generation.
Modular Architecture
Use prebuilt modules for NLP, ASR, TTS, and multimodal tasks.
Scalable Training
Supports Megatron-style training with data, tensor, and pipeline parallelism.
Speech AI
Build ASR and TTS systems with pretrained models and custom datasets.
How It Works
Install NeMo
Use `pip install nemo_toolkit` or build from source for full GPU support.
Choose a Domain
Select from NLP, ASR, TTS, or multimodal pipelines.
Load Pretrained Model
Use `from_pretrained()` to load models from NVIDIA NGC or Hugging Face.
Train or Fine-tune
Customize models with your data using PyTorch Lightning and Hydra configs.
Deploy with Triton
Export models and serve them using NVIDIA Triton Inference Server.
Code Example
from nemo.collections.nlp.models import TextClassificationModel
model = TextClassificationModel.from_pretrained("bert-base-uncased")
results = model.predict(["NeMo makes AI scalable."])
print(results)Use Cases
Enterprise Chatbots
Deploy scalable virtual assistants with domain-specific knowledge.
Speech Recognition
Transcribe audio using ASR models trained on multilingual datasets.
Text Classification
Categorize documents, emails, or support tickets using NLP pipelines.
Voice Synthesis
Generate lifelike speech using TTS models with emotional tone control.
Multimodal AI
Combine text, audio, and vision for rich, context-aware applications.
Integrations & Resources
Explore NVIDIA NeMo’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Popular Integrations
- PyTorch Lightning for training
- Hydra for configuration management
- NVIDIA Triton for inference
- TensorRT for optimized deployment
- NGC for pretrained models
- Hugging Face for model sharing
Helpful Resources
FAQ
Common questions about NVIDIA NeMo’s capabilities, usage, and ecosystem.