What Is The Difference Between NVIDIA NeMo Framework & NeMo Microservices?
The NVIDIA NeMo Framework and NeMo Microservices are two distinct components of NVIDIA's AI ecosystem, serving different purposes in the development & deployment of generative AI applications.
The NeMo Framework helps you create & train your AI models…
…while the NeMo Microservices provide the tools & infrastructure to deploy and manage those models in production, enabling AI Agents and other applications.
Here’s a clear breakdown of the differences…in the process of putting this content together, I am also in the process of building my own understanding. So if there is anything I got wrong or missed, please correct me.
Tomorrow I will be starting a series of blogs with practical examples on data curation and fine-tuning. Specifically on fine-tuning a model to be optimise the model for tool recognition and selection.
NVIDIA NeMo Framework
The NVIDIA NeMo Framework is an open-source, end-to-end, cloud-native framework designed for researchers and developers to build, customise and train generative AI models.
Including large language models (LLMs), multimodal models and speech AI (ASR, automatic speech recognition & text-to-speech).
The NeMo Framework is focused on the development and training of AI models from scratch or customising pre-trained models.
Model Training
It supports pre-training, supervised fine-tuning (SFT) and parameter-efficient fine-tuning (PEFT) techniques like LoRA, P-Tuning, and Adapters.
Data Curation
Includes tools like NeMo Curator for processing and preparing high-quality datasets (text, images, video) for training.
Scalability
Leverages advanced parallelism techniques and supports large-scale training across thousands of GPUs using tools like NeMo-Run.
Modularity
NeMo 2.0 introduces Python-based configurations and PyTorch Lightning’s modular abstractions for flexible experimentation.
Model Types
Supports LLMs (e.g., Nemotron, Llama, GPT), multimodal models and speech AI models (NeMo Canary, Parakeet etc.).
Deployment
Models trained with the NeMo Framework can be exported to optimised inference libraries (for example TensorRT-LLM, vLLM) or deployed with NVIDIA Riva or Triton Inference Server.
Use Case
Ideal for researchers and developers building custom AI models, experimenting with architectures, or pre-training models on proprietary datasets. For example, Amazon used the NeMo Framework to train its Titan LLMs.
Availability
Available as an open-source library on GitHub, a container on NVIDIA NGC, or part of NVIDIA AI Enterprise for enterprise-grade support.
NVIDIA NeMo Microservices
NVIDIA NeMo Microservices is modular set of containerised, enterprise-grade microservices designed to simplify the
customisation,
evaluation,
deployment and
operation
of large language models (LLMs) in production environments, typically on Kubernetes clusters.
NeMo microservices are focused on operationalising and scaling AI workflows by providing tools for fine-tuning, evaluation, inference, and safety in production settings.
Modular Components which includes microservices like:
NeMo Customizer
Simplifies fine-tuning LLMs using SFT and PEFT (for example LoRA, P-Tuning) for domain-specific use cases.
NeMo Evaluator
Evaluates LLMs on academic benchmarks, custom datasets or via LLM-as-a-Judge approaches.
NeMo Guardrails
Adds safety checks to prevent harmful outputs, hallucinations or jailbreak attempts.
NeMo Retriever
Enhances retrieval-augmented generation (RAG) by connecting models to business data for accurate responses.
NeMo Data Store
Provides file storage compatible with Hugging Face Hub APIs.
NeMo NIM Proxy and Operator
Manages inference and deployment of LLMs on Kubernetes.
Data Flywheel
Enables continuous improvement of AI models by cycling through data ingestion, training, evaluation and deployment, using inference data, business data and user feedback.
Deployment
Designed for production-grade deployment on-premises or in the cloud, often integrated with NVIDIA NIM (inference microservices) for optimised inference.
Enterprise Focus
Requires an NVIDIA AI Enterprise license for production use, ensuring security, stability, and support.
Use Case
Ideal for enterprises deploying and maintaining AI agents or applications in production, such as chatbots, virtual assistants, or customer service AI.
For example, AT&T uses NeMo Microservices to build a feedback-driven AI platform for customer care.
Availability
Available through the NVIDIA NGC catalog as part of NVIDIA AI Enterprise, with early access programs for some components.
High-level Data Flywheel Architecture Diagram
A data flywheel represents the lifecycle of models and data in a machine learning workflow. The process cycles through data ingestion, model training, evaluation, and deployment.
The diagram below illustrates how the NeMo microservices can construct a complete data flywheel.
In Closing
The NeMo Framework is a comprehensive toolset for researchers and developers to create and train generative AI models, offering flexibility for experimentation and large-scale training.
In contrast, NeMo Microservices are modular, production-focused tools for enterprises to fine-tune, evaluate, secure, and deploy AI models at scale, emphasising operational efficiency and continuous improvement via data flywheels. Together, they form a robust ecosystem for taking AI from research to real-world applications.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
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