# NVFP4

NVFP4 is a 4-bit data format and quantization recipe in NVIDIA Transformer Engine, using hierarchical block and global scaling for low-precision training.

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## Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer

DevFeed: [Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer](<https://devfeed.tech/articles/developing-nemotron-3-5-lightning-nvfp4-with-qad-using-nvidia-model-optimizer-6811.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/developing-nemotron-3-5-lightning-nvfp4-with-qad-using-nvidia-model-optimizer/>)

Author: Tanya Lenz

Published: 2026-08-17T18:12:48Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Post-training optimization](<https://devfeed.tech/topics/post-training-optimization.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Mamba](<https://devfeed.tech/topics/mamba.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [compute](<https://devfeed.tech/tags/compute.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [developers](<https://devfeed.tech/tags/developers.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [featured](<https://devfeed.tech/tags/featured.md>), [latency](<https://devfeed.tech/tags/latency.md>), [mamba](<https://devfeed.tech/tags/mamba.md>), [megatron](<https://devfeed.tech/tags/megatron.md>), [memory](<https://devfeed.tech/tags/memory.md>), [model](<https://devfeed.tech/tags/model.md>), [model-optimizer](<https://devfeed.tech/tags/model-optimizer.md>), [models](<https://devfeed.tech/tags/models.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [speed](<https://devfeed.tech/tags/speed.md>), [training](<https://devfeed.tech/tags/training.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

This tutorial explains how quantization-aware distillation (QAD) creates the Nemotron 3.5 Lightning NVFP4 checkpoint using NVIDIA Model Optimizer. It covers post-training quantization, teacher-student distillation, and evaluation, showing how QAD can recover accuracy while reducing memory usage and increasing throughput.

### Source excerpt

Teams customize their models to hit their targets for latency, speed, memory, and compute. With the open NVIDIA Nemotron family of models, developers can find...

## NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning

DevFeed: [NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning](<https://devfeed.tech/articles/nvidia-ising-enables-fully-automated-quantum-computer-calibration-with-enhanced-in-context-learning-6895.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-ising-enables-fully-automated-quantum-computer-calibration-with-enhanced-in-context-learning/>)

Author: Tanya Lenz

Published: 2026-07-27T16:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Ising](<https://devfeed.tech/topics/ising.md>), [Quantum Computing](<https://devfeed.tech/topics/quantum-computing.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [DGX Spark](<https://devfeed.tech/topics/dgx-spark.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [featured](<https://devfeed.tech/tags/featured.md>), [ising](<https://devfeed.tech/tags/ising.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [quantum-computing](<https://devfeed.tech/tags/quantum-computing.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

NVIDIA Ising Calibration 1.5 is an open-source vision-language model for interpreting quantum-processor diagnostics and recommending calibration actions. The article highlights zero-shot and in-context learning evaluation on QCalEval, plus an NVFP4-quantized version for local deployment.

### Source excerpt

NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they...

## Bringing Nunchaku 4-bit Diffusion Inference to Diffusers

DevFeed: [Bringing Nunchaku 4-bit Diffusion Inference to Diffusers](<https://devfeed.tech/articles/bringing-nunchaku-4-bit-diffusion-inference-to-diffusers-7368.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nunchaku-diffusers>)

Author: Pham Hong Vinh; Sayak Paul

Published: 2026-07-23T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [diffusers](<https://devfeed.tech/topics/diffusers.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [diffusion-transformers](<https://devfeed.tech/topics/diffusion-transformers.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>)

Tags: [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [diffusion-transformers](<https://devfeed.tech/tags/diffusion-transformers.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kernels](<https://devfeed.tech/tags/kernels.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [speed](<https://devfeed.tech/tags/speed.md>)

### AI overview

This article explains how Nunchaku brings 4-bit diffusion inference to Hugging Face Diffusers. It describes SVDQuant's W4A4 approach, integration of pre-quantized checkpoints without local CUDA compilation, and hardware requirements for NVFP4 and INT4 variants.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Holo3.1: Fast & Local Computer Use Agents

DevFeed: [Holo3.1: Fast & Local Computer Use Agents](<https://devfeed.tech/articles/holo3-1-fast-local-computer-use-agents-7004.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/Hcompany/holo31>)

Author: Maxime Langevin; Hamza Benchekroun; Axel Moyal; Emrick Sinitambirivoutin; Antonio Loison; Avshalom Manevich; Tony Wu; Pierre-Louis Cedoz; Aurélien Lac; Ronan Riochet

Published: 2026-06-02T14:13:23Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [computer-use](<https://devfeed.tech/topics/computer-use.md>), [On-device AI](<https://devfeed.tech/topics/on-device-ai.md>), [Local AI](<https://devfeed.tech/topics/local-ai.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [qwen](<https://devfeed.tech/topics/qwen.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [browser](<https://devfeed.tech/topics/browser.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [browser](<https://devfeed.tech/tags/browser.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [devices](<https://devfeed.tech/tags/devices.md>), [inference](<https://devfeed.tech/tags/inference.md>), [json](<https://devfeed.tech/tags/json.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [on-device](<https://devfeed.tech/tags/on-device.md>), [performance](<https://devfeed.tech/tags/performance.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [software](<https://devfeed.tech/tags/software.md>)

### AI overview

Holo3.1 is a family of computer-use models designed to operate across web, desktop, and mobile environments and integrate with different agent frameworks. The release adds quantized checkpoints for local inference, native function-calling support, and model sizes ranging from 0.8B to 35B-A3B, targeting private, cost-effective, and high-performance deployments.

### Source excerpt

Users want to run the same computer-use capabilities across desktop and mobile environments, with seamless integration with different agent frameworks. They want deployment flexibility, from cloud inference to fully local execution on end-user devices. This is why we are releasing the Holo3.1 family. Holo3.1 improves robustness across the three dimensions that matter most in production: environments (web, desktop, mobile), agent frameworks, and deployment targets.

## Introducing Mistral 3

DevFeed: [Introducing Mistral 3](<https://devfeed.tech/articles/introducing-mistral-3-7040.md>)

Original publisher: [Read original article](<https://mistral.ai/news/mistral-3/>)

Published: 2025-12-02T16:00:00Z

Content type: release

Language: en

Sources: [Mistral AI Blog](<https://devfeed.tech/sources/mistral-ai-blog.md>)

Topics: [AI Models](<https://devfeed.tech/topics/ai-models.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [vllm](<https://devfeed.tech/topics/vllm.md>), [Blackwell](<https://devfeed.tech/topics/blackwell.md>)

Tags: [apache](<https://devfeed.tech/tags/apache.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [mistral](<https://devfeed.tech/tags/mistral.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [models](<https://devfeed.tech/tags/models.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

Mistral announces Mistral 3, a family of open models including compact dense models and Mistral Large 3, a sparse mixture-of-experts model with 41B active and 675B total parameters. The models are released under Apache 2.0, with compressed formats and optimized checkpoints intended to improve accessibility, customization, and deployment across developer and enterprise environments.

### Source excerpt

The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.