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Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output. Stand up vLLM or SGLang serving on Jetson, using upstream vLLM on Thor and Orin JetPack 7.2+, and NVIDIA-AI-IOT vLLM on older Orin. Prebuilt, GPU-optimized model containers with a ready-to-use HTTP endpointIntermediate30 MINDeploy NVIDIA NIM for LLM Inference
Chat from the terminal against local vLLM with the self-improving Nous Research agent (Telegram optional)Intermediate30 MINRun Hermes Agent with a Local LLM
Build a local AI assistant in an OpenShell sandbox with vLLM inference and optional TelegramIntermediate30 MINRun NemoClaw with a Local LLM
Install a local-first AI agent and connect it to a private OpenAI-compatible model endpointIntermediate30 MINRun OpenClaw with a Local LLM
High-throughput serving for 30+ models, with continuous batching and an OpenAI-compatible APIIntermediate30 MINServe LLMs with vLLM
Local models reachable from your own machine's tools over an encrypted linkIntermediate30 MINServe LLMs with LM Studio
Chat, agents, coding, and document Q&A with Ollama, LM Studio, and AnythingLLM on your hardwareBeginner6 MINRun Local LLMs
High-throughput serving with RadixAttention, structured output, and an OpenAI-compatible APIIntermediate30 MINServe LLMs with SGLang
Choose a model and spin it up locally on your NVIDIA deviceAdvanced60 MINRun LLMs on Your NVIDIA Device
One interface for supervised, RLHF, and parameter-efficient trainingIntermediate60 MINFine-Tune LLMs with LLaMA Factory
LoRA, full training, and RL paths plus Nemotron open modelsIntermediate8 MINFine-Tune Specialized LLMs with Unsloth
A self-hosted browser interface with models running locally on your GPUBeginner15 MINChat with LLMs Using Open WebUI and Ollama
Industry leading jailbreak classification model for protection from adversarial attemptsDownloadablenemoguard-jailbreak-detect
Diffusion-based 26B parameter LLM enabling parallel token generation for real-time text appsDownloadableFree Endpointdiffusiongemma-26b-a4b-it
Build advanced AI agents for providers and patients using this developer example powered by NeMo Microservices, NVIDIA Nemotron, Riva ASR and TTS, and NVIDIA LLM NIMHealthcare & Life SciencesLaunchableDeveloper ExampleAmbient Healthcare Agents
Multimodal 320B-total / 18B-active MoE with hybrid KDA and sparse MLA attention, native FP8 weights, reasoning and tool calling.Free Endpointglm-5-3-flash
How to swap the VLM in the VSS Alerts Blueprint — covers RTVI-VLM microservice deployment methods, all three VLM consumers (rtvi-vlm, vlm-as-verifier, vss-agent), and health checks. Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples. Extract false-positive and false-negative gaps from VLM binary-classification-question (BCQ, yes/no) predictions. Use when the user asks to "analyze VLM BCQ gaps", "extract VLM false positives and false negatives", or identify failure cases from a predict
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