# NVIDIA RTX

Published articles for NVIDIA RTX.

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

## Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX

DevFeed: [Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX](<https://devfeed.tech/articles/perplexity-portable-computer-is-now-available-on-windows-powered-by-nvidia-rtx-21586.md>)

Original publisher: [Read original article](<https://blogs.nvidia.com/blog/local-ai-perplexity-windows-pcs/>)

Author: Gerardo Delgado

Published: 2026-09-14T15:00:52Z

Content type: news

Language: en

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

Topics: [Local AI](<https://devfeed.tech/topics/local-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [NVIDIA RTX](<https://devfeed.tech/topics/nvidia-rtx.md>), [Windows](<https://devfeed.tech/topics/windows.md>), [GeForce](<https://devfeed.tech/topics/geforce.md>), [qwen](<https://devfeed.tech/topics/qwen.md>), [NVIDIA DGX](<https://devfeed.tech/topics/nvidia-dgx.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [Slack](<https://devfeed.tech/topics/slack.md>), [Google](<https://devfeed.tech/topics/google.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [drive](<https://devfeed.tech/tags/drive.md>), [geforce](<https://devfeed.tech/tags/geforce.md>), [github](<https://devfeed.tech/tags/github.md>), [google](<https://devfeed.tech/tags/google.md>), [local-ai](<https://devfeed.tech/tags/local-ai.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [nvidia-dgx](<https://devfeed.tech/tags/nvidia-dgx.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [rtx-pro](<https://devfeed.tech/tags/rtx-pro.md>), [rtx-spark](<https://devfeed.tech/tags/rtx-spark.md>), [slack](<https://devfeed.tech/tags/slack.md>), [windows](<https://devfeed.tech/tags/windows.md>)

### AI overview

Perplexity is adding Portable Computer to its Windows app for compatible NVIDIA GeForce RTX PCs and NVIDIA RTX PRO Workstations. The local agent uses NVIDIA-accelerated models to plan multistep tasks, analyze files, and keep sensitive information on the device, while users can authorize cloud support for more advanced research and reasoning.

### Source excerpt

As local models become more capable, AI agents can handle more work directly on a PC while keeping sensitive information on the device. Portable Computer is a local version of the agent Perplexity Computer that plans and carries out multistep tasks. Accelerated by NVIDIA GPUs, it uses local models to analyze data, bring together information [...]

## Eaton HDXL Rack PDU Review: 81kW From a Single Zero-U PDU for AI Racks

DevFeed: [Eaton HDXL Rack PDU Review: 81kW From a Single Zero-U PDU for AI Racks](<https://devfeed.tech/articles/eaton-hdxl-rack-pdu-review-81kw-from-a-single-zero-u-pdu-for-ai-racks-12383.md>)

Original publisher: [Read original article](<https://www.storagereview.com/review/eaton-hdxl-rack-pdu-review-81kw-from-a-single-zero-u-pdu-for-ai-racks>)

Author: Brian Beeler

Published: 2026-09-10T19:23:54Z

Content type: comparison

Language: en

Sources: [StorageReview.com](<https://devfeed.tech/sources/storagereview-com.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [systems](<https://devfeed.tech/topics/systems.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Server](<https://devfeed.tech/topics/server.md>), [dell](<https://devfeed.tech/topics/dell.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [NVIDIA RTX](<https://devfeed.tech/topics/nvidia-rtx.md>), [Blackwell](<https://devfeed.tech/topics/blackwell.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [dell](<https://devfeed.tech/tags/dell.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [launch](<https://devfeed.tech/tags/launch.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [power-management](<https://devfeed.tech/tags/power-management.md>), [review](<https://devfeed.tech/tags/review.md>), [server](<https://devfeed.tech/tags/server.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

This review examines Eaton's HDXL Rack PDU G4, a zero-U power distribution unit designed to deliver 56-81 kW for high-density AI racks. It explains how GPU server power requirements, outlet counts, and redundant A/B feeds make this class of PDU necessary, using Dell PowerEdge systems with high-end accelerators as an example.

### Source excerpt

Eaton is getting ready to ship the kind of PDU that didn't need to exist five years ago. The HDXL Rack PDU G4, arriving in the fourth quarter of 2026, delivers 56-81kW from a single zero-U unit, with 24 outlets, 21 branch breakers, and a 100A corded or 140A terminal-block input. Four models launch in The post Eaton HDXL Rack PDU Review: 81kW From a Single Zero-U PDU for AI Racks appeared first on StorageReview.com.

## Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026

DevFeed: [Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026](<https://devfeed.tech/articles/sparks-fly-nvidia-accelerates-local-ai-at-ifa-2026-6954.md>)

Original publisher: [Read original article](<https://blogs.nvidia.com/blog/local-ai-ifa-next-gen-agents-nv-pair-rtx-spark/>)

Author: Gerardo Delgado

Published: 2026-09-03T16:00:59Z

Content type: news

Language: en

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

Topics: [Local AI](<https://devfeed.tech/topics/local-ai.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [local-ai](<https://devfeed.tech/tags/local-ai.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [rtx-ai-garage](<https://devfeed.tech/tags/rtx-ai-garage.md>), [rtx-spark](<https://devfeed.tech/tags/rtx-spark.md>), [video](<https://devfeed.tech/tags/video.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

NVIDIA announces local-AI updates at IFA 2026, including agent tooling, faster local inference, RTX Spark Windows PCs, and locally runnable models for agentic, coding, and video-generation workloads.

### Source excerpt

Frontier intelligence is going local. At IFA 2026, NVIDIA, Microsoft and its partners are teaming up to provide faster inference and new tools that make agents easier to set up and run locally on NVIDIA hardware. New compact NVIDIA RTX Spark Windows PCs are also coming in October to give AI enthusiasts, developers and creators [...]

## Leading Publishers Bring Blockbuster PC Games and Technology to NVIDIA RTX Spark

DevFeed: [Leading Publishers Bring Blockbuster PC Games and Technology to NVIDIA RTX Spark](<https://devfeed.tech/articles/leading-publishers-bring-blockbuster-pc-games-and-technology-to-nvidia-rtx-spark-6948.md>)

Original publisher: [Read original article](<https://blogs.nvidia.com/blog/gamescom-rtx-spark-pc-games-technology/>)

Author: Alexander Mejia

Published: 2026-08-25T15:30:48Z

Content type: news

Language: en

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

Topics: [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [conference](<https://devfeed.tech/tags/conference.md>), [developers](<https://devfeed.tech/tags/developers.md>), [game-development](<https://devfeed.tech/tags/game-development.md>), [gamescom](<https://devfeed.tech/tags/gamescom.md>), [gaming](<https://devfeed.tech/tags/gaming.md>), [geforce](<https://devfeed.tech/tags/geforce.md>), [geforce-now](<https://devfeed.tech/tags/geforce-now.md>), [generation](<https://devfeed.tech/tags/generation.md>), [launch](<https://devfeed.tech/tags/launch.md>), [model](<https://devfeed.tech/tags/model.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [pc](<https://devfeed.tech/tags/pc.md>), [performance](<https://devfeed.tech/tags/performance.md>), [platform](<https://devfeed.tech/tags/platform.md>), [ray-tracing](<https://devfeed.tech/tags/ray-tracing.md>), [rtx-spark](<https://devfeed.tech/tags/rtx-spark.md>), [technology](<https://devfeed.tech/tags/technology.md>)

### AI overview

NVIDIA is bringing RTX Spark gaming PCs to Gamescom with support from major publishers including Electronic Arts, Embark and Ubisoft. The platform combines NVIDIA RTX technologies, Windows gaming, anti-cheat support and AI-enabled capabilities, with a launch planned for this fall.

### Source excerpt

NVIDIA is bringing the next wave of RTX gaming to the Gamescom conference running this week in Cologne, Germany, with support for new games, anti-cheat technologies and increased visual quality. Electronic Arts, Embark and Ubisoft are among the latest game publishers and developers bringing their blockbuster titles to NVIDIA RTX Spark ahead of its launch [...]

## Q&A: How Capcom Brought Path Tracing to RE ENGINE Across PRAGMATA and Resident Evil Requiem

DevFeed: [Q&A: How Capcom Brought Path Tracing to RE ENGINE Across PRAGMATA and Resident Evil Requiem](<https://devfeed.tech/articles/q-a-how-capcom-brought-path-tracing-to-re-engine-across-pragmata-and-resident-evil-requiem-6923.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/qa-how-capcom-brought-path-tracing-to-re-engine-across-pragmata-and-resident-evil-requiem/>)

Author: Michelle Horton

Published: 2026-07-16T22:59:09Z

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: [Game Development](<https://devfeed.tech/topics/game-development.md>), [Game engine](<https://devfeed.tech/topics/game-engine.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [content-creation-rendering](<https://devfeed.tech/tags/content-creation-rendering.md>), [development](<https://devfeed.tech/tags/development.md>), [featured](<https://devfeed.tech/tags/featured.md>), [game-development](<https://devfeed.tech/tags/game-development.md>), [gaming](<https://devfeed.tech/tags/gaming.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [pc](<https://devfeed.tech/tags/pc.md>), [ray-tracing](<https://devfeed.tech/tags/ray-tracing.md>), [ray-tracing-path-tracing](<https://devfeed.tech/tags/ray-tracing-path-tracing.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [technology](<https://devfeed.tech/tags/technology.md>), [unreal-engine](<https://devfeed.tech/tags/unreal-engine.md>)

### AI overview

Capcom's RE ENGINE team developed and integrated a real-time path tracer for PRAGMATA and Resident Evil Requiem. Built on the NVIDIA RTX Kit and optimized for DLSS, the renderer replaces shadow-map direct lighting, improves continuity between gameplay and cutscenes, and enables more cohesive indirect lighting, reflections, and strand-hair light transmission.

### Source excerpt

Capcom's RE ENGINE team set out to bring path tracing into two shipping titles at once, Resident Evil Requiem and PRAGMATA, each with a different visual...

## Using local LLMs for agentic coding

DevFeed: [Using local LLMs for agentic coding](<https://devfeed.tech/articles/using-local-llms-for-agentic-coding-29082.md>)

Original publisher: [Read original article](<https://blog.alexewerlof.com/p/local-llms-for-agentic-coding>)

Author: Alex Ewerlöf

Published: 2026-06-04T09:01:34Z

Content type: tutorial

Language: en

Sources: [Alex Ewerlof Notes](<https://devfeed.tech/sources/alex-ewerlof-notes.md>)

Topics: [coding](<https://devfeed.tech/topics/coding.md>), [agentic-coding](<https://devfeed.tech/topics/agentic-coding.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [GitHub Copilot CLI](<https://devfeed.tech/topics/github-copilot-cli.md>), [Linux](<https://devfeed.tech/topics/linux.md>), [llama.cpp](<https://devfeed.tech/topics/llama-cpp.md>), [Ollama](<https://devfeed.tech/topics/ollama.md>), [NVIDIA RTX](<https://devfeed.tech/topics/nvidia-rtx.md>)

Tags: [agentic-coding](<https://devfeed.tech/tags/agentic-coding.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [claude](<https://devfeed.tech/tags/claude.md>), [coding](<https://devfeed.tech/tags/coding.md>), [github](<https://devfeed.tech/tags/github.md>), [linux](<https://devfeed.tech/tags/linux.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llama-cpp](<https://devfeed.tech/tags/llama-cpp.md>), [llms](<https://devfeed.tech/tags/llms.md>), [local](<https://devfeed.tech/tags/local.md>), [local-llms](<https://devfeed.tech/tags/local-llms.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [ollama](<https://devfeed.tech/tags/ollama.md>), [quality](<https://devfeed.tech/tags/quality.md>), [rocm](<https://devfeed.tech/tags/rocm.md>), [state](<https://devfeed.tech/tags/state.md>)

### AI overview

A practical guide to using local language models for agentic coding. It covers running local models, configuring coding agents such as Copilot and Pi, and evaluating local models against cloud models in terms of cost, privacy, tooling, and performance.

### Source excerpt

AI honeymoon pricing is over, but your work is not

## (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware

DevFeed: [(LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware](<https://devfeed.tech/articles/lora-fine-tuning-flux-1-dev-on-consumer-hardware-7203.md>)

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

Author: Derek Liu; Marc Sun; Sayak Paul; merve; Linoy Tsaban

Published: 2025-06-19T00:00:00Z

Content type: tutorial

Language: en

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

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [flux](<https://devfeed.tech/topics/flux.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [dev](<https://devfeed.tech/tags/dev.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [flux](<https://devfeed.tech/tags/flux.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>), [lora](<https://devfeed.tech/tags/lora.md>), [model-training](<https://devfeed.tech/tags/model-training.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

This tutorial explains how to fine-tune the FLUX.1-dev diffusion model efficiently with QLoRA on a single consumer GPU using less than about 10 GB of VRAM. It describes the model components, focuses training on the transformer while keeping the text encoders and VAE frozen, and discusses LoRA, quantization, and FP8 training for memory and speed improvements.

### Source excerpt

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

## I Rebuilt My Home Server -- Quieter, Faster, and Ready for 2025

DevFeed: [I Rebuilt My Home Server -- Quieter, Faster, and Ready for 2025](<https://devfeed.tech/articles/i-rebuilt-my-home-server-quieter-faster-and-ready-for-2025-10553.md>)

Original publisher: [Read original article](<https://technotim.com/posts/home-server-upgrade-2025/>)

Author: Techno Tim

Published: 2025-04-12T13:00:00Z

Content type: opinion

Language: en

Sources: [Techno Tim](<https://devfeed.tech/sources/techno-tim.md>)

Topics: [Homelab](<https://devfeed.tech/topics/homelab.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [servers](<https://devfeed.tech/topics/servers.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [intel](<https://devfeed.tech/topics/intel.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [NVIDIA RTX](<https://devfeed.tech/topics/nvidia-rtx.md>)

Tags: [cpu](<https://devfeed.tech/tags/cpu.md>), [files](<https://devfeed.tech/tags/files.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [home-server](<https://devfeed.tech/tags/home-server.md>), [homelab](<https://devfeed.tech/tags/homelab.md>), [intel](<https://devfeed.tech/tags/intel.md>), [lighting](<https://devfeed.tech/tags/lighting.md>), [noctua](<https://devfeed.tech/tags/noctua.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [pwm](<https://devfeed.tech/tags/pwm.md>), [server](<https://devfeed.tech/tags/server.md>), [ssd](<https://devfeed.tech/tags/ssd.md>), [upgrades](<https://devfeed.tech/tags/upgrades.md>)

### AI overview

Techno Tim describes rebuilding a home server with upgraded CPU, GPU, RAM, fans, SSDs, and power supply. The article says the rebuilt system is quieter and faster, and lists hardware including Noctua cooling components, Intel products, NVIDIA GPUs, ECC RAM, and SSDs.

### Source excerpt

I rebuilt my home server from the ground up: CPU, GPU, RAM, fans, SSDs, and power supply... even some "accessory lighting". This thing is now quieter, faster, and built to handle just about anything I throw at it in 2025 and hopefully beyond. Thank you to Noctua for helping to make this server quiet! Thank you to Patrick from Serve the Home for the CPU upgrades! (Both Noctua and Patrick gifted...

## What's new in TensorFlow 2.18

DevFeed: [What's new in TensorFlow 2.18](<https://devfeed.tech/articles/what-s-new-in-tensorflow-2-18-7415.md>)

Original publisher: [Read original article](<https://blog.tensorflow.org/2024/10/whats-new-in-tensorflow-218.html>)

Author: TensorFlow Blog (noreply@blogger.com)

Published: 2024-10-28T19:00:00Z

Content type: release

Language: en

Sources: [The TensorFlow Blog](<https://devfeed.tech/sources/the-tensorflow-blog.md>)

Topics: [Tensorflow](<https://devfeed.tech/topics/tensorflow.md>), [releases](<https://devfeed.tech/topics/releases.md>), [LiteRT](<https://devfeed.tech/topics/litert.md>), [NumPy](<https://devfeed.tech/topics/numpy.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [cudnn](<https://devfeed.tech/topics/cudnn.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [announcement](<https://devfeed.tech/tags/announcement.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cudnn](<https://devfeed.tech/tags/cudnn.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [litert](<https://devfeed.tech/tags/litert.md>), [migration](<https://devfeed.tech/tags/migration.md>), [nccl](<https://devfeed.tech/tags/nccl.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [python](<https://devfeed.tech/tags/python.md>), [release](<https://devfeed.tech/tags/release.md>), [releases](<https://devfeed.tech/tags/releases.md>), [reproducible-builds](<https://devfeed.tech/tags/reproducible-builds.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>), [tensorflow-core](<https://devfeed.tech/tags/tensorflow-core.md>)

### AI overview

TensorFlow 2.18 introduces NumPy 2.0 compatibility updates, transitions TFLite development to the LiteRT repository, and adds hermetic CUDA, cuDNN, and NCCL dependencies for more reproducible source builds. Binary packages add dedicated kernels for compute capability 8.9 GPUs, including NVIDIA RTX 40 series, L4, and L40, while dropping precompiled support for Maxwell GPUs.

### Source excerpt

Posted by the TensorFlow team TensorFlow 2.18 has been released! Highlights of this release (and 2.17) include NumPy 2.0, LiteRT repository, CUDA Update, Hermetic CUDA and more. For the full release notes, please click here. Note: Release updates on the new multi-backend Keras will be published on keras.io, starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core NumPy 2.0 The upcoming TensorFlow 2.18 release will include support for NumPy 2.0. While the majority of TensorFlow APIs will function seamlessly with NumPy 2.0, this may break some edge cases of usage, e.g., out-of-boundary conversion errors and numpy scalar representation errors. You can consult the following common solutions. Note that NumPy's type promotion rules have been changed (See NEP 50 for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results. Please see the NumPy 2 migration guide. We've updated some TensorFlow tensor APIs to maintain compatibility with NumPy 2.0 while preserving the out-of-boundary conversion behavior in NumPy 1.x. LiteRT Repository We're making some changes to how LiteRT (formerly known as TFLite) is developed. Over the coming months, we'll be gradually transitioning TFLite's codebase to LiteRT. Once the migration is complete, we'll start accepting contributions directly through the LiteRT repository. There will no longer be any binary TFLite releases and developers should switch to LiteRT for the latest updates. Hermetic CUDA If you build TensorFlow from source, Bazel will now download specific versions of CUDA, CUDNN and NCCL distributions, and then use those tools as dependencies in various Bazel targets. This enables more reproducible builds for Google ML projects and supported CUDA versions because the build no longer relies on the locally installed versions. More details are provided here. CUDA Update TensorFlow binary distributions now ship with d

## Running Darknet Object Detection on an NVIDIA RTX 3090

DevFeed: [Running Darknet Object Detection on an NVIDIA RTX 3090](<https://devfeed.tech/articles/rtx-3090-for-machine-learning-10501.md>)

Original publisher: [Read original article](<https://technotim.com/posts/3090-machine-learning/>)

Author: Techno Tim

Published: 2021-01-30T14:00:00Z

Content type: tutorial

Language: en

Sources: [Techno Tim](<https://devfeed.tech/sources/techno-tim.md>)

Topics: [NVIDIA RTX](<https://devfeed.tech/topics/nvidia-rtx.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [object-detection](<https://devfeed.tech/topics/object-detection.md>), [Ubuntu](<https://devfeed.tech/topics/ubuntu.md>), [Windows](<https://devfeed.tech/topics/windows.md>)

Tags: [homelab](<https://devfeed.tech/tags/homelab.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [object-detection](<https://devfeed.tech/tags/object-detection.md>), [ubuntu](<https://devfeed.tech/tags/ubuntu.md>), [windows](<https://devfeed.tech/tags/windows.md>)

### AI overview

A tutorial on compiling and running the open-source Darknet neural network on an NVIDIA RTX 3090 in Windows and Ubuntu Linux for object detection on images and real-time video.

### Source excerpt

The NVIDIA RTX 3090 is a beast.We all know it can beat the benchmarks in gaming, but how about machine learning and neural networks? Today we walk through the RTX 3090 and then compile and run Darknet, an open source neural network, on Windows and then Ubuntu Linux and run object detection on pictures, images, and real-time video.You will be amazed at how much more you can get out of your vide...