# NVIDIA NIM NVIDIA NIM is a set of microservices for deploying foundation models on clouds or data centers, with production-grade runtimes and APIs. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS DevFeed: [Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS]() Original publisher: [Read original article]() Author: Maryam Motamedi; Mikyas Desta; Jason Li; Jason Roche Published: 2026-08-10T16:25:36Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Low Latency](), [Latency](), [voice ai](), [NVIDIA NIM](), [Open Source Models & Datasets](), [asr](), [Conversational AI](), [Large Language Model](), [Automation]() Tags: [agents](), [ai](), [api](), [audio](), [automation](), [conversational-ai](), [data](), [developers](), [infrastructure](), [latency](), [llm](), [low-latency](), [model](), [models](), [nvidia](), [nvidia-nim](), [open](), [speech](), [text-to-speech](), [voice]() ### AI overview This developer article presents NVIDIA Magpie Multilingual TTS as an open-weights, 364M-parameter text-to-speech model for building low-latency multilingual voice applications. It explains how a self-managed cascaded ASR, TTS, and LLM architecture can provide deployment control, tuning, privacy, and predictable performance across 12 languages. ### Source excerpt Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS Every voice interaction has a latency budget. By the time a user hears your application respond, you've already spent precious milliseconds capturing audio, transcribing speech, running an LLM, retrieving context, and generating a response. Text-to-speech (TTS) is the final step -- and the one users notice most. If speech generation is slow, the whole experience feels slow. ## Build a Domain-Specific Embedding Model in Under a Day DevFeed: [Build a Domain-Specific Embedding Model in Under a Day]() Original publisher: [Read original article]() Author: Steve Han; Rucha Apte; Sean Sodha; Oliver Holworthy Published: 2026-03-20T19:38:16Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Fine-tuning](), [synthetic-data](), [Synthetic Data Generation](), [Model Development](), [Inference](), [model-deployment](), [NeMo](), [Nemotron](), [NVIDIA NIM](), [TensorRT](), [Claude](), [API keys]() Tags: [api](), [fine-tuning](), [gpu](), [inference](), [nemo](), [nemotron](), [nim](), [nvidia](), [nvidia-nim](), [synthetic](), [synthetic-data](), [synthetic-data-generation]() ### AI overview A tutorial showing how to fine-tune a general-purpose embedding model for a specific domain in less than a day using synthetic question-answer pairs generated from domain documents. It covers data generation, contrastive training, retrieval evaluation, and deployment, using NVIDIA NeMo components and a Llama-Nemotron embedding model. ### Source excerpt With a single GPU and less than a day of training time, you can transform a general-purpose embedding model into one that truly understands your domain, no manual labeling required. To help you hit the ground running, we are also releasing a ready-to-use synthetic training dataset generated from NVIDIA's public documentation using this exact pipeline. ## Accelerate a World of LLMs on Hugging Face with NVIDIA NIM DevFeed: [Accelerate a World of LLMs on Hugging Face with NVIDIA NIM]() Original publisher: [Read original article]() Author: Neal Vaidya Published: 2025-07-21T18:01:30Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [NVIDIA NIM](), [hugging face](), [Deployment](), [Inference](), [Docker Container](), [TensorRT-LLM](), [sglang](), [vllm]() Tags: [cuda](), [deployment](), [docker](), [hugging-face](), [inference](), [llms](), [nim](), [nvidia-nim](), [sglang](), [tensorrt-llm](), [vllm]() ### AI overview This tutorial explains how NVIDIA NIM can deploy a broad range of LLMs from Hugging Face using a single Docker container. It covers supported checkpoint formats, inference frameworks, environment prerequisites, authentication, caching, permissions, and local model deployment. ### Source excerpt NVIDIA AI customers and ecosystem partners leverage NVIDIA NIM inference microservices to streamline deployment of the latest AI models on NVIDIA accelerated infrastructure, including LLMs, multi-modal and domain-specific models from NVIDIA, Meta, Mistral AI, Google and hundreds more innovative model builders. ## Serverless Inference with Hugging Face and NVIDIA NIM DevFeed: [Serverless Inference with Hugging Face and NVIDIA NIM]() Original publisher: [Read original article]() Author: Philipp Schmid; Jeff Boudier Published: 2024-07-29T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [NVIDIA NIM](), [hugging face](), [Inference](), [model-serving](), [Serverless](), [DGX Cloud](), [Large Language Model](), [Generative AI](), [API](), [llama](), [Access Control](), [Claude]() Tags: [access-control](), [api](), [cloud](), [dgx-cloud](), [generative-ai](), [hardware](), [hugging-face](), [inference](), [llama](), [llm](), [nvidia](), [nvidia-dgx](), [nvidia-nim](), [partnerships](), [pricing](), [serverless]() ### AI overview Hugging Face announces a serverless NVIDIA NIM API on the Hugging Face Hub for Enterprise Hub organizations. The service provides API access to open generative AI models, including Llama and Mistral, on NVIDIA DGX Cloud infrastructure, with pay-as-you-go pricing and a guide for creating fine-grained organization tokens. The article notes that the service was deprecated and unavailable as of April 10, 2025. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.