… NVIDIA TensorRT Edge-LLM , a high-performance C++ inference runtime for LLMs and vision language models VLMs on embedded platforms, is designed to overcome these challenges. …
… Now that your NIM is running locally, we need to update the agent you created in rag agent.py to use it. llm = ChatNVIDIA base url="http://nemotron:8000/v1", model=LLM MODEL, temperature=0.6, top p=0.95, max tokens=8192 With your langgraph server still running, go back to our Simple Agents Client a… …
… 13 MIN READ Feb 09, 2026 Automating Inference Optimizations with NVIDIA TensorRT LLM AutoDeploy NVIDIA TensorRT LLM enables developers to build high-performance inference engines for large language models LLMs , but deploying a new architecture... …
… It’s also worth noting that SLMs are not the weaker siblings of LLMs. Newer SLM models show performance comparable to or even exceeding that of much larger LLMs in targeted benchmarks such as commonsense reasoning, tool calling, and instruction following. …
… Get started with emerging optimizers for LLM training Higher-order optimizers like Muon are proving essential for pushing the boundaries of LLM training efficiency. …
… The following code example shows how to use it. from nemo.deploy.nlp import NemoQueryLLM nq = NemoQueryLLM url="localhost:8000", model name="llama3 70b fp8", nq.query llm prompts= "How does PTQ work?" , top k=1, Llama 3 PTQ example and results For demonstration purposes, we present Llama 3 PTQ thro… …