Topaz alternative 🤏 SeedVR2+TensorRT faster VAE Processing. VRGDG SeedVR2 TensorRT Studio turns the SeedVR2 pipeline into a practical Windows workflow: load a video, test preview, compare the result frame by frame, and complete long renders 👇

We just released TensorRT Model Connect in Public Preview. You can take a supported @huggingface model to end-to-end TensorRT inference in just two commands. No intermediate ONNX export, and the resulting bundle can run through native C++ APIs. We also built the entire project with @OpenAIDevs Codex agents, with humans directing and reviewing the work. That includes model implementations, performance tuning, tests, integrations, and docs. It’s open source, so go try it out, dig into the implementations, or contribute support for a new model:

Run #LLMs# on @NVIDIARobotics #Jetson# without x86 ONNX export. 🚀 #TensorRT-Model-Connect# on reComputer Classic J5012 (#AGXOrin# 64GB, #JP7#.2). @huggingface checkpoint → trtmc build → .bundle on device trtmc run for text gen Tested: #Qwen3-4B# FP16 (~41GB container peak, 64GB recommended) No ONNX hop. Edge LLM/agent ready. Would you use on-device TRTMC for prototyping, or keep ONNX export for production? 👇 Full guide: Get your own one: #NVIDIA# #Jetson# #TensorRT# #TRTMC# #AGXOrin# #EdgeAI# #LLM# #PhysicalAI# #SeeedStudio#

What if every decode step gave the next one a head start? Meet Guess-Verify-Refine — a new hardware-aware sparse-attention algorithm from NVIDIA Research. Built for TensorRT LLM on Blackwell, it reuses temporal patterns across decode steps for: → 1.88x faster Top-K attention → 9.3% better end-to-end latency in low-latency serving Dive into the paper:

This blog post from @nvidia is intended to develop a clearer way of reasoning about performance optimization for PyTorch models and other representations. It explores questions such as: What makes one compiled engine better than another? Why can individually promising decisions fail end to end? How should accuracy constrain performance optimization? It introduces the Global Performance Tuner, a feature for automatic performance tuning released in TensorRT 11.1, and explores these questions through an intuitive example and a mathematical framework. Read the full blog post here:

3/ muse spark open is available today through API platforms at Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI. model weights are available for download on Amazon SageMaker AI, Google Cloud Model Garden, Hugging Face, and Microsoft Foundry Model Catalog. it runs on leading inference engines like SGLang, vLLM, TensorRT-LLM, Hugging Face Transformers, Apple MLX, llama.cpp, and Unsloth with optimizations for AMD, Apple, Intel, and NVIDIA silicon. developers can easily further customize the model using the PyTorch framework.

If you already have 4x DGX Sparks, you might not missing out much waiting for the 512 GB M5 Ultra. Here's why: Memory capacity: both 512 GB unified. Peak memory bandwidth: 4x Sparks: ~1092 GB/s aggregate (TP=4) M5 Ultra: ~1,200 GB/s They're almost identical on what matters most for local LLMs - memory capacity and bandwidth. Where the DGX Sparks still pull ahead Prefill is generally stronger, concurrency scales more cleanly, and the entire CUDA ecosystem (vLLM, TensorRT-LLM, etc.) is simply more mature for multi-node inference right now. Video and image generation will also probably be noticeably faster on the sparks due to CUDA. CUDA is still king. The Mac is cleaner and more efficient as a single box. But if you already own the 4x DGX Sparks, you can continue sleeping well.

Shopee just published the results of building its own frontier LLM, which now powers use cases from search and personalization to supply chain. Compass, Shopee's specialized model for Southeast Asian ecommerce, scaled from 3 billion to 340 billion monthly API tokens in eight months on our AI factory stack. It includes NVIDIA Megatron-Core for pretraining, NVIDIA NeMo framework for post-training, and TensorRT-LLM for inference, running across NVIDIA A100, H100, and RTX PRO 6000 Blackwell GPUs. Results: ✅ Compass API tokens scale 113x in eight months ✅ 50x faster fraud detection at 90% lower processing cost Read the customer story.

Open weights are table stakes now So here's the boring infra nobody wants to tweet about, also open⚡️ Things we've open sourced: — CubeSandbox, per-conversation sandboxes with browser, code, shell and file access, spun up and torn down for you — Agent Memory, a shared memory layer across your agents, and now across your teammates' agents too Things we've upstreamed: — RoCE optimizations for DeepSeek's DeepEP, doubling throughput on commodity networks, with 30% carrying back to InfiniBand — FlexKV, tiered KV cache offloading, now in NVIDIA Dynamo, vLLM and TensorRT-LLM mainlines All four are searchable by name, repos and docs. Pull requests welcome, and so are complaints 🛠️