# NVFP4

Published articles for NVFP4.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine

DevFeed: [Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine](<https://devfeed.tech/articles/accelerating-dropless-moe-training-in-jax-with-nvidia-transformer-engine-21079.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/accelerating-dropless-moe-training-in-jax-with-nvidia-transformer-engine/>)

Author: Tanya Lenz

Published: 2026-09-14T16:39:15Z

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: [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [GB200](<https://devfeed.tech/topics/gb200.md>), [Network](<https://devfeed.tech/topics/network.md>), [Python](<https://devfeed.tech/topics/python.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>), [mixtral](<https://devfeed.tech/topics/mixtral.md>), [qwen](<https://devfeed.tech/topics/qwen.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [communication](<https://devfeed.tech/tags/communication.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [mixtral](<https://devfeed.tech/tags/mixtral.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [moe](<https://devfeed.tech/tags/moe.md>), [networks](<https://devfeed.tech/tags/networks.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [python](<https://devfeed.tech/tags/python.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [router](<https://devfeed.tech/tags/router.md>), [routing](<https://devfeed.tech/tags/routing.md>), [tensors](<https://devfeed.tech/tags/tensors.md>), [token](<https://devfeed.tech/tags/token.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

This article explains how NVIDIA Transformer Engine and JAX kernel optimizations accelerate dropless Mixture of Experts (MoE) training. It describes bottlenecks from token routing, expert dispatch and gathering, all-to-all communication, and ragged expert matrix operations. In DeepSeek-V3 training on NVIDIA GB200, the optimized approach increased performance from 103 to 1,068 TFLOPS per GPU, a 10.4x improvement.

### Source excerpt

Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE...

## Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM

DevFeed: [Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM](<https://devfeed.tech/articles/deploying-qwen3-8-2-4t-a95b-on-amazon-sagemaker-hyperpod-with-vllm-4731.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/deploying-qwen3-8-2-4t-a95b-on-amazon-sagemaker-hyperpod-with-vllm/>)

Author: Dmitry Soldatkin

Published: 2026-09-09T22:26:29Z

Content type: tutorial

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Deployment](<https://devfeed.tech/topics/deployment.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-hyperpod](<https://devfeed.tech/tags/amazon-sagemaker-hyperpod.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [inference](<https://devfeed.tech/tags/inference.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [moe](<https://devfeed.tech/tags/moe.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>), [tool](<https://devfeed.tech/tags/tool.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

A deployment walkthrough for serving the open-weight Qwen3.8-2.4T-A95B language model on Amazon SageMaker HyperPod with vLLM and NVIDIA B300 GPUs. It covers provisioning, NVFP4 quantization, an OpenAI-compatible endpoint, reasoning, tool calling, and MTP speculative decoding.

### Source excerpt

Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding.

## When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving

DevFeed: [When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving](<https://devfeed.tech/articles/when-to-use-encode-prefill-decode-disaggregation-to-accelerate-multimodal-model-serving-6945.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/when-to-use-encode-prefill-decode-disaggregation-to-accelerate-multimodal-model-serving/>)

Author: Tanya Lenz

Published: 2026-09-09T20:31:04Z

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: [Optimization](<https://devfeed.tech/topics/optimization.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Dynamo](<https://devfeed.tech/topics/dynamo.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [computer-vision-video-analytics](<https://devfeed.tech/tags/computer-vision-video-analytics.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [dynamo](<https://devfeed.tech/tags/dynamo.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [low-latency-inference](<https://devfeed.tech/tags/low-latency-inference.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [model-serving](<https://devfeed.tech/tags/model-serving.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [optimization](<https://devfeed.tech/tags/optimization.md>)

### AI overview

This article explains when encode-prefill-decode disaggregation improves multimodal model serving. Using NVIDIA Dynamo, it separates vision encoding from LLM prefill and decode so the stages can batch, schedule, and scale independently. The approach is most effective for image-heavy prompts, short-to-medium outputs, and quantized mixture-of-experts models, with reported gains of up to 5x in time to first token and 7x in end-to-end response time.

### Source excerpt

Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill...

## Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson

DevFeed: [Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson](<https://devfeed.tech/articles/frontier-reasoning-reaches-the-edge-how-to-deploy-and-optimize-models-on-nvidia-jetson-6826.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/frontier-reasoning-reaches-the-edge-how-to-deploy-and-optimize-models-on-nvidia-jetson/>)

Author: Elizabeth Goodman

Published: 2026-09-04T16:21:04Z

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: [Jetson](<https://devfeed.tech/topics/jetson.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [edge](<https://devfeed.tech/tags/edge.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [jetpack](<https://devfeed.tech/tags/jetpack.md>), [jetson](<https://devfeed.tech/tags/jetson.md>), [jetson-orin](<https://devfeed.tech/tags/jetson-orin.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [thor](<https://devfeed.tech/tags/thor.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

A tutorial on deploying and optimizing compact reasoning and agentic AI models on NVIDIA Jetson. It covers choosing models, improving inference with NVFP4 quantization and speculative decoding, serving example models with vLLM, and validating a configuration for a workload.

### Source excerpt

Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run...

## Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference

DevFeed: [Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference](<https://devfeed.tech/articles/co-designing-ai-models-using-speculative-decoding-for-faster-llm-inference-6781.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/co-designing-ai-models-using-speculative-decoding-for-faster-llm-inference/>)

Author: Tanya Lenz

Published: 2026-09-02T16:04:19Z

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: [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [batch](<https://devfeed.tech/tags/batch.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

The article explains speculative decoding as a way to speed up LLM inference while preserving standard-decoding outputs. A smaller draft model proposes several tokens, which the larger target model verifies in parallel; it also defines draft and acceptance lengths and gives a speedup formula.

### Source excerpt

This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and...

## 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...

## Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72

DevFeed: [Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72](<https://devfeed.tech/articles/serve-qwen3-8-2-4t-a95b-a-2-4t-parameter-model-with-configurable-reasoning-on-nvidia-gb300-nvl72-6938.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/serve-qwen3-8-2-4t-a95b-a-2-4t-parameter-model-with-configurable-reasoning-on-nvidia-gb300-nvl72/>)

Author: Michelle Horton

Published: 2026-08-12T18:23:13Z

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: [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [cache](<https://devfeed.tech/tags/cache.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [model](<https://devfeed.tech/tags/model.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [routing](<https://devfeed.tech/tags/routing.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### AI overview

The article describes serving Alibaba's open-weight Qwen3.8-2.4T-A95B model on NVIDIA GB300 NVL72 systems for large-scale reasoning and agentic workloads.

### Source excerpt

Alibaba released the open weights for Qwen3.8-2.4T-A95B (Qwen3.8-Max), its largest open-weight model, bringing near-frontier capabilities to the open...

## NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents

DevFeed: [NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents](<https://devfeed.tech/articles/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents-6899.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents/>)

Author: Tanya Lenz

Published: 2026-08-11T13:01:07Z

Content type: release

Language: en

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

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [featured](<https://devfeed.tech/tags/featured.md>), [inference](<https://devfeed.tech/tags/inference.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [nemoclaw](<https://devfeed.tech/tags/nemoclaw.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [routing](<https://devfeed.tech/tags/routing.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### AI overview

NVIDIA introduces Nemotron 3.5 Lightning, an open 30B MoE model with 3B active parameters for fast, high-volume execution in long-running AI agents. It also presents NeMo Switchyard for routing tasks to appropriate models.

### Source excerpt

Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning...

## 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.

## Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI

DevFeed: [Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI](<https://devfeed.tech/articles/inside-nvidia-rubin-gpu-architecture-powering-the-era-of-agentic-ai-6863.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/inside-nvidia-rubin-gpu-architecture-powering-the-era-of-agentic-ai/>)

Author: Eduardo Alvarez

Published: 2026-07-21T18:15:00Z

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: [GPU](<https://devfeed.tech/topics/gpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [datacenter](<https://devfeed.tech/topics/datacenter.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [cache](<https://devfeed.tech/tags/cache.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [dsx](<https://devfeed.tech/tags/dsx.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [latency](<https://devfeed.tech/tags/latency.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [memory](<https://devfeed.tech/tags/memory.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [rubin-gpu](<https://devfeed.tech/tags/rubin-gpu.md>), [scale](<https://devfeed.tech/tags/scale.md>), [tensor-cores](<https://devfeed.tech/tags/tensor-cores.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [training](<https://devfeed.tech/tags/training.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

This article examines the NVIDIA Rubin GPU architecture and its co-designed Vera Rubin platform for agentic AI inference. It describes how Tensor Cores, HBM4 memory, the Transformer Engine, NVFP4 performance, cache, decoding, and scale-up systems address throughput, latency, long-context execution, and rack-scale deployment.

### Source excerpt

What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale....

## 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.

## LLM Inference Benchmarking - Measure What Matters

DevFeed: [LLM Inference Benchmarking - Measure What Matters](<https://devfeed.tech/articles/llm-inference-benchmarking-measure-what-matters-19901.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/llm-inference-benchmarking>)

Author: Rithish Ramesh

Published: 2026-02-06T14:46:06Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [cache](<https://devfeed.tech/tags/cache.md>), [compute](<https://devfeed.tech/tags/compute.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [moe](<https://devfeed.tech/tags/moe.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [routing](<https://devfeed.tech/tags/routing.md>)

### AI overview

This article examines LLM inference benchmarking as a complex systems problem spanning hardware and software. It focuses on how latency, throughput, concurrency, and cost interact, and distinguishes the compute-bound prefill phase from the memory-bound decode phase.

### Source excerpt

Production-grade LLM inference is a complex systems challenge, requiring deep co-designs - from hardware primitives (FLOPs, memory bandwidth, and interconnects) to sophisticated software layers - across the entire stack. Given the hardware variability across GPU providers like NVIDIA and AMD - including generational differences in numeric type performance (FP8, BF16, NVFP4 etc), HBM bandwidth and capacity, peak FLOPs etc - optimal performance is never guaranteed. It depends on the software's ability to maximize FLOPs utilization during prefill, maximize bandwidth efficiency during decode, optimize expert routing in MoE models, discover optimal parallelism strategies, and more. As inference hardware costs remain high, squeezing maximum performance to improve unit economics is a primary objective for AI teams. We are currently in an era of intense hardware-software co-design that will redefine performance and cost efficiency. Consequently, benchmarking must evolve to track three critical pillars: end-to-end model performance, micro-benchmarking of isolated components and a structured way to go after performance improvements. This article focuses on the LLM performance domain and analyzes the interplay between latency, throughput, concurrency, and cost. Prefill and Decode: The two phases of Inference LLM Inference works in two-phases: prefill and decode. The prefill phase is where the entire input goes through the model's forward pass which includes self-attention, add & norm, and pass through the hidden layers of the model's feed forward network. This phase is extremely compute bound. FLOPs per byte transferred (arithmetic intensity) for the prefill phase is very high. In simpler terms, the GPU is spending more time computing than waiting for the data from memory. On the other hand, the decode phase is memory bound. For every token that is generated, decode needs to load the entire weight matrix, KV cache from the HBM, generate one token, and write it back to the HBM

## 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.