AWS re:Invent 2025 - Tapping into the Power of Agentic AI: Driving Mission Success with NVIDIA & AWS
Source: Dev.to
Introduction
Hello, everyone—thanks for joining. In this session we’ll explore how NVIDIA and AWS collaborate on generative‑AI, agentic design, and mission‑critical workloads. We’ll cover the evolution from large language models (LLMs) to retrieval‑augmented generation (RAG), parameter‑efficient fine‑tuning, and finally autonomous agents, as well as the tooling and infrastructure needed to move from proof‑of‑concept to production.
Evolution of Generative AI
From LLMs to RAG
- January 2023 – Widespread adoption of LLMs; users began interacting with models via natural language.
- RAG – Retrieval‑augmented generation adds external knowledge sources, improving relevance and effectiveness for mission‑specific tasks.
Parameter‑Efficient Fine‑Tuning
- Techniques such as LoRA (Low‑Rank Adaptation) enable rapid adaptation of large models with minimal additional parameters, reducing training cost and time.
Autonomous Agents
- Agents orchestrate multiple specialized models (or “modules”) to accomplish complex workflows.
- They form an ecosystem rather than a single monolithic entity, allowing flexible composition and scaling.
NVIDIA Terminology
| Term | Meaning | Role |
|---|---|---|
| NEMO | Neural Modules | Modular building blocks for AI pipelines; enable reusable components across tasks. |
| NIM | NVIDIA Inference Microservice | Optimized container images (including NVIDIA drivers, RAPIDS, etc.) for fast inference of frontier or Hugging Face models. |
| Blueprints | Helm‑chart collections | Define how multiple containers (e.g., NIMs) are deployed on Kubernetes, simplifying lift‑and‑shift to production. |
Agent Architecture
- Orchestrator – Central “avatar” that decides which sub‑agents or tools to invoke.
- Tool Use & Computer Use – Agents can call external APIs, run code, or interact with other services.
- Memory – Persistent state that grows quickly; proper management is essential to avoid token explosion.
- Token Growth – Agent reasoning can generate an exponential number of tokens, making debugging and cost control challenging.
Scaling Challenges
- Exponential Token Growth – Leads to higher latency and cost; requires careful prompting and token‑budget strategies.
- Security & Authentication – Both human users and compute resources must be authenticated at scale.
- Data Governance – Heterogeneous data sources demand robust profiling, lineage, and access controls.
- Legacy Code Integration – Existing pipelines often contain disparate components that must be unified under a common framework.
These factors create a “chasm” where proof‑of‑concepts falter when moved to production. Addressing them early—through defensive programming, data validation, and secure orchestration—helps avoid performance degradation.
NVIDIA NeMo Agent Toolkit
- Modular, framework‑agnostic: Works with PyTorch, TensorFlow, and other ecosystems.
- Productivity gains:
- ~57 % fewer lines of code compared to custom implementations.
- 16× faster data processing with NeMo Curator.
- 2× faster response times for agent queries.
Demonstrated Capabilities
- Parameter‑efficient fine‑tuning (LoRA) on large models.
- End‑to‑end deployment on AWS services such as Amazon EKS (Kubernetes) and Amazon SageMaker.
Deployment Options
| AWS Service | Use Case |
|---|---|
| Amazon EKS | Run Kubernetes clusters with NeMo Blueprints and Helm charts for scalable, containerized agents. |
| Amazon SageMaker | Managed training and inference for large models; integrates with NIM containers for low‑latency serving. |
| AWS Marketplace | Pre‑built NeMo containers and Blueprints available for quick provisioning. |
Getting Started
- GitHub / NGC: Free access to NeMo modules, Blueprints, and container images via the NVIDIA NGC registry.
- Documentation & Resources:
- Build and deploy agents at build.nvidia.com
- Find ready‑to‑use containers on the AWS Marketplace
Conclusion
By leveraging NVIDIA’s modular AI stack (NeMo, NIMs, Blueprints) together with AWS’s scalable infrastructure, organizations can accelerate the transition from experimental AI agents to robust, production‑grade solutions that drive mission success. Proper attention to token management, security, and data governance ensures that agents remain reliable and cost‑effective as they scale.