#NIM

Articles about NIM — exploring patterns, best practices, and real-world implementations in production systems.

5 posts tagged with nim. ← All posts

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Pratik Dhanave · ·12 min read

Production on the NVIDIA Stack

Taking an NVIDIA-stack LLM system from a working prototype to something you trust in production — reliability, cost and throughput, observability, and security — all from Python, with the OpenAI-compatible surface keeping the code stable whether you burst to the API Catalog or run your own NIM.

Run an NVIDIA-stack LLM system in production from Python: hosted vs self-hosted vs hybrid, reliability (client retries, tenacity backoff, readiness probes, fallback), the GPU-hours cost model, Prometheus observability across the pipeline, and securing nvapi-/NGC keys.

#NVIDIA#Production#NIM#Python#AI Engineering#Software Engineering#LLMs#Observability
Pratik Dhanave · ·15 min read

Self-Hosting and Optimizing Inference

When to graduate from the hosted API Catalog to inference you run yourself — deploying a NIM container, reaching for Triton and its Python client, the TensorRT-LLM levers that raise throughput and cut latency, and the one base_url change that carries your Python client across unchanged.

Move from the hosted API Catalog to self-hosted inference: run a NIM container (your Python client works unchanged by swapping base_url), use Triton directly via tritonclient, and understand the TensorRT-LLM levers — quantization, in-flight batching, paged KV cache, tensor parallelism.

#NVIDIA#NIM#Performance#Python#AI Engineering#Software Engineering#LLMs#APIs
Pratik Dhanave · ·12 min read

Tool Calling with NIM

Driving function calling against NVIDIA NIM models from Python — the full request-execute-respond loop with the plain `openai` client, then the same thing automated by `ChatNVIDIA.bind_tools`.

Function/tool calling against NIM models in Python: the openai round-trip loop (json.loads the arguments string, echo tool_call_id, cap the iterations) and the LangChain ChatNVIDIA.bind_tools path — with the honest caveat that model support varies.

#NVIDIA#NIM#Tool Use#Python#AI Engineering#Software Engineering
Pratik Dhanave · ·10 min read

Calling NIM from Python

Your first real NVIDIA NIM calls in Python, two idiomatic ways: the plain OpenAI SDK pointed at NVIDIA's endpoint, and the official LangChain integration — with error handling, streaming, and the one change that makes the same code run against a self-hosted model.

Make your first NIM calls in Python two ways: the standard openai client pointed at the API Catalog with an nvapi- key, and the official ChatNVIDIA from langchain-nvidia-ai-endpoints — with streaming, token usage, typed errors, and the base_url swap to a self-hosted NIM.

#NVIDIA#NIM#LLMs#Python#AI Engineering#Software Engineering#Streaming#APIs
Pratik Dhanave · ·8 min read

The NVIDIA AI Stack

A Python engineer's map of NVIDIA's AI platform — NIM inference, NeMo Retriever, NeMo Guardrails, and Triton with TensorRT-LLM — and why the same code runs against the hosted API Catalog or your own self-hosted microservice.

The opener to a series on building LLM and agent applications on NVIDIA's AI stack from Python: how NIM, the API Catalog, NeMo Retriever, NeMo Guardrails, Triton and TensorRT-LLM fit together — and why the openai client and langchain-nvidia-ai-endpoints make it Python-native.

#NVIDIA#NIM#LLMs#Python#AI Engineering#Software Engineering#Guardrails#APIs

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.