# Local AI Artificial intelligence workloads, including model inference and agents, run on local devices or systems rather than exclusively in the cloud. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AMD's GAIA Local AI Now Able To Transcribe & Summarize Meeting Recordings DevFeed: [AMD's GAIA Local AI Now Able To Transcribe & Summarize Meeting Recordings]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T10:19:10Z Content type: news Language: en Sources: [Phoronix]() Topics: [gaia](), [Local AI](), [Generative AI](), [Artificial Intelligence]() Tags: [agents](), [ai](), [bug-fixes](), [desktop-linux](), [gaia](), [generative-ai](), [github](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [local-ai](), [macos](), [open-source](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [release](), [security](), [ubuntu-benchmarks](), [ubuntu-hardware](), [user-interface](), [windows]() ### AI overview AMD GAIA 0.24 adds local transcription and summarization of meeting recordings, including multi-speaker recognition. The release also improves inbox triage and its text interface, supports an embedded local Lemonade server, and includes security and bug fixes. ### Source excerpt AMD's GAIA software for building AI agents on your PC and leveraging generative AI locally with the power of AMD Ryzen and Radeon hardware continues becoming more featureful. Out today is AMD GAIA 0.24/0.24.1 and with it comes the ability to transcribe and summarize meeting recordings locally along with other functionality... ## HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard DevFeed: [HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-15T23:15:57Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [Local AI](), [Hardware](), [GPU](), [cpu](), [Large Language Model]() Tags: [consumer](), [cpu](), [deepseek](), [dgx-spark](), [gemma](), [gpu](), [hardware](), [local-ai](), [models](), [ollama](), [windows](), [workstation]() ### AI overview StorageReview previews HP's pre-production ZBook Ultra G3a 16, a local AI laptop with 192GB of unified memory and up to 160GB assignable to its integrated GPU. The article examines its hardware and planned testing while noting that shipping-hardware benchmarks are not yet available. ### Source excerpt HP's ZBook Ultra G1a 14 holds the Best for Large Models spot on our Best Laptops for Local AI leaderboard because its 128GB of unified memory, 96GB of it assignable to the GPU, loaded models no discrete-GPU laptop could touch. The new HP ZBook Ultra G3a 16 raises that pool to 192GB with up to The post HP ZBook Ultra G3a 16 Preview: 192GB of Unified Memory Aims for the Top of the Local AI Laptop Leaderboard appeared first on StorageReview.com. ## How and Why We Bought 4x DGX Sparks DevFeed: [How and Why We Bought 4x DGX Sparks]() Original publisher: [Read original article]() Author: Alex Ellis Published: 2026-09-15T00:00:00Z Content type: opinion Language: en Sources: [Alex Ellis' Blog]() Topics: [Local AI](), [GPU](), [qwen](), [Cloud]() Tags: [agents](), [ai](), [cloud](), [gpu](), [llm](), [local-ai](), [localai](), [qwen](), [red-teaming](), [security]() ### AI overview The author explains why OpenFaaS Ltd bought four DGX Sparks and what the team learned from deploying local AI. The article argues that local infrastructure can provide tangible privacy and risk-reduction benefits for business use cases, even though it is not primarily justified by cost per token. ### Source excerpt In June we deployed an RTX 6000 Pro into production, a few weeks later, we're now operating DGX Sparks for the team. Learn how and why. ## 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]() Original publisher: [Read original article]() Author: Gerardo Delgado Published: 2026-09-14T15:00:52Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [Local AI](), [Artificial Intelligence](), [NVIDIA RTX](), [Windows](), [GeForce](), [qwen](), [NVIDIA DGX](), [Cloud](), [GitHub](), [Slack](), [Google](), [Microsoft]() Tags: [agentic-ai](), [ai](), [ai-agents](), [cloud](), [dgx-spark](), [drive](), [geforce](), [github](), [google](), [local-ai](), [microsoft](), [nvidia-dgx](), [nvidia-rtx](), [open-source](), [qwen](), [rtx-pro](), [rtx-spark](), [slack](), [windows]() ### 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 [...] ## Mastering Edge AI on Raspberry Pi with LiteRT and Gemma DevFeed: [Mastering Edge AI on Raspberry Pi with LiteRT and Gemma]() Original publisher: [Read original article]() Author: Lu Wang; Terry Heo; Naushir Patuck; José María Casanova Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [LiteRT](), [On-device AI](), [Local AI](), [AI, ML & Data Engineering](), [Google AI](), [cpu](), [Security, Privacy and Abuse Prevention]() Tags: [cli](), [cpu](), [edge](), [gemma](), [gpu](), [inference](), [litert](), [offline](), [on-device](), [real-time](), [robotics]() ### AI overview The article explains how to deploy Gemma models with LiteRT on a Raspberry Pi for local, real-time edge AI applications such as robotics. It highlights LiteRT-LM, CPU and GPU optimization, and reported performance figures for Gemma 4 E2B on Raspberry Pi 5. ### Source excerpt Deploying secure, real-time Edge AI on Raspberry Pi is now simplified using LiteRT and lightweight Gemma open models. LiteRT optimizes CPU and GPU performance, delivering fast token speeds for models like Gemma4, enabling real-time local reasoning for robotics. Developers can quickly convert, quantize, and run these models using the lightweight LiteRT CLI tool. Support for Hailo AI accelerators is also coming very soon. ## NVIDIA Personal AI Router Distributes AI Tasks across Local Compute DevFeed: [NVIDIA Personal AI Router Distributes AI Tasks across Local Compute]() Original publisher: [Read original article]() Author: Sergio De Simone Published: 2026-09-11T15:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Local AI](), [Inference](), [GPU](), [Hardware]() Tags: [agent](), [agents](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [compute](), [demo](), [development](), [gpu](), [inference](), [large-language-models](), [local](), [ml-data-engineering](), [news](), [node](), [nvidia](), [nvidia-pair-ai-task-router](), [open-source](), [orchestration](), [performance]() ### AI overview NVIDIA has introduced PAIR in beta, a local router that distributes inference requests across compatible computers for multi-agent AI workloads. It works with local inference services such as Ollama and LM Studio and selects a node based on model and engine requirements. ### Source excerpt NVIDIA Personal AI Router (PAIR), now available in beta, lets you combine the inference capacity of multiple computers on your local network and automatically distribute AI requests among them. It is primarily designed for local multi-agent AI workloads, where multiple independent model calls can otherwise overwhelm one GPU. By Sergio De Simone ## Use a local and open source code assistant DevFeed: [Use a local and open source code assistant]() Original publisher: [Read original article]() Author: Seth Kenlon Published: 2026-09-09T14:01:45Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [ai-coding](), [Local AI](), [Open Source Models & Datasets](), [Large Language Model](), [Model Context Protocol](), [ide](), [Security, Privacy and Abuse Prevention](), [Command-line interface](), [Homebrew](), [Linux](), [macOS]() Tags: [ai-coding](), [ai-inference](), [artificial-intelligence](), [command-line](), [developer-tools](), [ide](), [linux](), [llm](), [local-ai](), [macos](), [model-context-protocol](), [ollama](), [open-source](), [privacy]() ### AI overview This Red Hat Developer article explains how to use OpenCode as a local, open source AI coding assistant. It covers OpenCode's terminal, desktop, and IDE extension interfaces, its use of the Model Context Protocol, installation requirements, and the need to configure an LLM. For privacy-conscious local development, it recommends open source local AI tools such as Ollama or OpenVINO. ### Source excerpt There's a lot of excitement about AI coding assistants, but many of the available options either aren't open source, or don't respect your data privacy by sending what you're working on to the cloud for processing. If you're looking for an alternative to closed AI, then you need an open coding assistant and an open source IDE. The post Use a local and open source code assistant appeared first on Red Hat Developer. ## Rust AI in Practice: Building LLM Applications With Rig DevFeed: [Rust AI in Practice: Building LLM Applications With Rig]() Original publisher: [Read original article]() Author: Irina Mihajlovic Published: 2026-09-09T12:02:05Z Content type: article Language: en Sources: [The JetBrains Blog]() Topics: [Language models](), [Local AI](), [AI-assisted coding]() Tags: [ai](), [ai-agents](), [anthropic](), [applications](), [gemini](), [livestream](), [llm](), [llm-aplications](), [openai](), [rag](), [rust](), [rust-ai](), [rustrover]() ### AI overview The article introduces Rig, a Rust library that provides a unified interface for building LLM applications across providers. It uses a livestream coding-agent demo to explain Rig's provider clients, models, agents, tools, and prompts. ### Source excerpt Rust AI is moving from the experimentation stage toward more practical usage. We recently kicked off a new livestream series with the Rust Foundation to explore how Rust and AI are coming together in real-world applications. In the first session, our Developer Advocate Orhun Parmaksız spoke with Stephen Korzeniewski, Lead Maintainer of Rig at 0xPlaygrounds. [...] ## Pair Programming With Your AI Agent DevFeed: [Pair Programming With Your AI Agent]() Original publisher: [Read original article]() Author: blundell Published: 2026-09-06T21:50:09Z Content type: tutorial Language: en Sources: [Blundell]() Topics: [AI Agent](), [Local AI](), [Programming](), [Command-line interface]() Tags: [agent](), [ai](), [ai-agent](), [androiddev](), [antigravity](), [cli](), [gemini](), [geminicli](), [intermediate](), [intermediate-reference-ai-androiddev-gemini-geminicli-pairing](), [pair](), [pairing](), [programming](), [reference](), [terminal]() ### AI overview This tutorial presents sessions with a local AI agent as pair programming and maps four classic pairing styles--unstructured, Driver/Navigator, Strong-Style Pairing, and Ping Pong--to AI-assisted development. It uses Antigravity CLI for examples while noting that the approach is not specific to that tool. ### Source excerpt This post explains how to treat a session with a local AI agent as a pair programming session, and how the four classic pairing styles map onto working with one. I'll use Antigravity CLI for the concrete examples, though nothing here is specific to it. Swap in whichever agent sits in your terminal. The idea [...] The post Pair Programming With Your AI Agent first appeared on Blundell. ## Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026 DevFeed: [Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026]() Original publisher: [Read original article]() Author: Gerardo Delgado Published: 2026-09-03T16:00:59Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [Local AI](), [Inference](), [Hardware](), [Inference Performance](), [Mixture of Experts (MoE)]() Tags: [agentic-ai](), [agents](), [ai](), [ai-inference](), [announcements](), [artificial-intelligence](), [dgx-spark](), [hardware](), [inference](), [llama-cpp](), [local-ai](), [mixture-of-experts-moe](), [nvidia](), [nvidia-rtx](), [open-source](), [rtx-ai-garage](), [rtx-spark](), [video](), [vllm]() ### 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 [...] ## NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network DevFeed: [NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-03T16:00:00Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference](), [Local AI](), [GPU](), [Hardware]() Tags: [ai-agent](), [ai-agents](), [ai-inference](), [compute](), [content-creation-rendering](), [dgx-spark](), [edge-computing](), [gaming](), [geforce](), [gpu](), [hardware](), [inference](), [local](), [nvidia](), [open-source]() ### AI overview NVIDIA PAIR is a beta virtual inference router that distributes independent local inference requests across eligible machines on a home network. It works through compatible Ollama and LM Studio interfaces without requiring changes to an agent harness. ### Source excerpt AI agents are learning to do more by working together. A lead agent can break a complex task into smaller jobs and assign those jobs to specialized subagents.... ## Choosing Local Models for Coding Agents Based on Hardware and Workload DevFeed: [Choosing Local Models for Coding Agents Based on Hardware and Workload]() Original publisher: [Read original article]() Author: Avi Chawla Published: 2026-09-02T19:10:34Z Content type: tutorial Language: en Sources: [Daily Dose of Data Science]() Topics: [Local AI](), [Hardware](), [Artificial Intelligence](), [Inference](), [quantization](), [Open Source]() Tags: [ai](), [hardware](), [local](), [local-ai](), [model](), [open-source](), [quantization](), [run]() ### AI overview This practitioner's guide explains why local models that work well for chat may perform poorly in coding-agent workloads. It discusses growing conversation context, memory requirements, precision, sustained speed, and thermal limits, then introduces Magnitude, an open-source inference server that profiles a machine and selects a configuration for local agent use. ### Source excerpt A practitioner's guide to local AI. ## Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI DevFeed: [Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI]() Original publisher: [Read original article]() Author: Nico Martin; Joshua Published: 2026-09-01T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [webgpu](), [Local AI](), [Machine learning](), [Inference](), [benchmarking](), [GPU](), [Optimization]() Tags: [benchmark](), [gpu](), [hub](), [inference](), [javascript](), [kernels](), [local-ai](), [open-source](), [optimization](), [shaders](), [testing](), [webgpu]() ### AI overview Hugging Face releases @huggingface/kernels, a JavaScript library and collection of 207 versioned WebGPU kernel packages for browser-based local AI. It also introduces Fleet, a browser benchmarking and testing suite that gathers opted-in performance and correctness evidence across hardware. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Build with Tailscale. Build on Tailscale. DevFeed: [Build with Tailscale. Build on Tailscale.]() Original publisher: [Read original article]() Author: Kevin Purdy Published: 2026-08-31T14:00:00Z Content type: article Language: en Sources: [Blog on Tailscale]() Topics: [networking](), [Local AI](), [Go](), [App](), [ide]() Tags: [apis](), [applications](), [dev](), [go](), [local](), [local-ai](), [networking](), [server](), [software]() ### AI overview Tailscale describes two ways to build with its networking platform: embedding secure, identity-aware connectivity into applications with tsnet, and automating tailnet creation and management through APIs. A local Ollama server illustrates how application-level identities, ACLs, MagicDNS, and managed HTTPS can avoid public ports, host daemons, and manual proxy configuration. ### Source excerpt Put secure networking inside what you build, then automate the rest. ## Meet editorial-guide-ramalama: An AI Assistant That Checks Your Fedora CommBlog and Magazine Articles Against Editorial Guidelines DevFeed: [Meet editorial-guide-ramalama: An AI Assistant That Checks Your Fedora CommBlog and Magazine Articles Against Editorial Guidelines]() Original publisher: [Read original article]() Author: Ananya Nalavathu Published: 2026-08-25T12:47:18Z Content type: article Language: en Sources: [Fedora Community Blog]() Topics: [Fedora](), [Retrieval Augmented Generation (RAG)](), [Artificial Intelligence](), [Local AI](), [Containers](), [Open Source](), [Tooling](), [On-device AI]() Tags: [ai](), [articles](), [blog](), [fedora-project-community](), [inference](), [local](), [mentored-projects](), [oci](), [open-source](), [rag](), [tool]() ### AI overview The article introduces editorial-guide-ramalama, a Retrieval-Augmented Generation assistant built for Fedora contributors. It checks drafts against Fedora's editorial guidelines and published articles, cites specific guidelines when it identifies problems, and suggests actionable fixes. The tool uses RamaLama to run open models locally as OCI containers, with built-in ingestion, chunking, and retrieval, avoiding API keys and external services. ### Source excerpt By Ananya Nalavathu and Francois Gonothi Toure Introduction Open source communities run on contribution, and contribution runs on documentation, storytelling, and knowledge sharing. At the Fedora Project, that means the Fedora Community Blog and Fedora Magazine, two publications that give contributors a voice and give the community a way to stay informed, inspired, and connected. [...] The post Meet editorial-guide-ramalama: An AI Assistant That Checks Your Fedora CommBlog and Magazine Articles Against Editorial Guidelines appeared first on Fedora Community Blog. ## Building a Local, Multimodal AI Terminal Agent with Gemma 4 DevFeed: [Building a Local, Multimodal AI Terminal Agent with Gemma 4]() Original publisher: [Read original article]() Author: Arjun Prabhulal Published: 2026-08-12T09:25:11Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [gemma4](), [Artificial Intelligence](), [multimodal-ai](), [Command-line interface](), [Large Language Model](), [Local AI](), [Code](), [Google]() Tags: [ai](), [ai-agent](), [artificial-intelligence](), [code](), [function-calling](), [gemma](), [gemma-4](), [google](), [google-cloud-platform](), [local-ai](), [multimodal](), [ollama](), [open-source](), [terminal]() ### AI overview A tutorial introduces Gemma 4 and builds a local multimodal terminal agent named gemma4-agent. It covers function calling, tool orchestration, text, image, and voice processing, plus Gemma 4's model variants and architecture. ### Source excerpt Introduction Open-source LLM models have been improving rapidly with tool calling, extended context windows, and native vision and audio capabilities, all while delivering strong benchmark performance. Gemma 4, recently introduced by Google Deepmind brings all of these features together in sizes efficient enough to run locally. In this article, we'll look at the capabilities of Gemma 4 and build a multimodal (Text, Vision, Voice) CLI agent (gemma4-agent) with function-calling capabilities. By the end, you'll have an agent that can chat, write, execute code, analyze images, and process voice instructions to deliver highly grounded responses. What is Gemma 4 Model ? Gemma 4 is Google DeepMind's open model family, released in April 2026 under the Apache 2.0 license. Built from the same research and technology behind Gemini 3, Gemma 4 is designed for high-performance reasoning, coding, multimodal understanding, and local AI execution across different model sizes. Features of Gemma 4 Models Improved Tool calling : Native function calling and tool orchestration, letting agents act autonomously without bloating prompt instructions Thinking mode : Built-in step-by-step thinking mode via the <|think|> token for complex multi-turn logic Context Windows : Up to 256K tokens on the 12B and larger models (128K on the edge-sized E2B/E4B) for processing long document and tool outputs Extended Multimodality : Gemma 4 models can process text,voice and images simultaneously like extracting data from charts, analyzing screenshots , and reviewing UI mockups. Gemma 4 Model Variants & SpecificationsGemma 4 Architecture Gemma 4 comes in five model sizes built around four architectural variants, each making different trade-offs between performance, inference speed, compute, and memory. Gemma4 Unified 12B vs Effective Parameters Effective-parameter models (E2B and E4B) are dense transformer models optimized for edge and on-device deployment. The "E" stands for effective parameters use Per-La ## Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA DevFeed: [Run Local Agentic AI Workflows with Meta's Muse Glimmer on NVIDIA]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-10T13:27:19Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Local AI](), [Large Language Model](), [long-context](), [Inference](), [Meta](), [Open Source](), [GPU](), [NVIDIA DGX](), [NVLink](), [Jetson](), [Automation]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [ai](), [automation](), [context-window](), [data-center-cloud](), [dgx-spark](), [dgx-station](), [edge-computing](), [featured](), [gpu](), [inference](), [jetson](), [local-ai](), [nemoclaw](), [nvidia](), [nvidia-dgx](), [nvlink](), [open-source](), [top-stories]() ### AI overview Meta's Muse Glimmer is a 30B open-weight dense model designed for local agentic AI workflows. With a 120K+ context window and performance of up to 20K tokens per second on a single GPU, it supports sustained, multi-step tool use and local processing of sensitive data. ### Source excerpt Meta returns to the open source ecosystem with the release of Muse Glimmer, a 30B open-weight dense model with a 120K+ context window built for local AI... ## 11 Underrated Self-Hosted Apps You Can Run on a Raspberry Pi DevFeed: [11 Underrated Self-Hosted Apps You Can Run on a Raspberry Pi]() Original publisher: [Read original article]() Author: Karen Rangi Published: 2026-07-11T05:00:00Z Content type: article Language: en Sources: [RaspberryTips]() Topics: [App](), [Open Source](), [Docker](), [dashboards](), [Local AI](), [data](), [Monitoring]() Tags: [article](), [docker](), [inspiration](), [local-ai](), [open-source](), [raspberry-pi](), [self-hosted]() ### AI overview A hands-on overview of self-hosted applications that can run on a Raspberry Pi, including photo and video management, productivity, file management, and monitoring tools. The article highlights Docker-based deployment and discusses Immich as a local-AI photo-management option and Homepage as a customizable dashboard. ### Source excerpt I have been running several self-hosted applications on my Raspberry Pi for some time now. From my experience, it's one of the best ways to take back control of your data while ditching costly cloud subscriptions. In this article, I will share the best self-hosted apps you can run on a Raspberry Pi, based on... ## What a Raspberry Pi Can (and Can't) Do With AI DevFeed: [What a Raspberry Pi Can (and Can't) Do With AI]() Original publisher: [Read original article]() Author: Patrick Fromaget Published: 2026-06-24T11:54:14Z Content type: article Language: en Sources: [RaspberryTips]() Topics: [Artificial Intelligence](), [Local AI](), [Large language models (LLMs)](), [object-detection](), [OpenClaw]() Tags: [ai](), [applications](), [computer-vision](), [cpu](), [gpu](), [hardware](), [large-language-models-llms](), [local](), [local-ai](), [object-detection](), [openclaw](), [quick-tips](), [raspberry-pi]() ### AI overview This article explains what Raspberry Pi devices can and cannot do with AI. They can run applications such as object detection, computer vision projects, lightweight AI agents, and some small language models, but limited computing power makes most modern LLMs slow or impractical locally. AI HAT and AI Camera products can improve computer vision workloads but do little for LLM execution. ### Source excerpt AI (artificial intelligence) is a buzzword that has been thrown around a lot these days, and the Raspberry Pi ecosystem is no exception. New use cases have been tested on it, and new products have even been released to accompany this phenomenon. So, what can your Raspberry Pi actually do with AI? A Raspberry Pi... ## DiffusionGemma: 4x faster text generation DevFeed: [DiffusionGemma: 4x faster text generation]() Original publisher: [Read original article]() Author: Brendan O'Donoghue Published: 2026-06-10T16:24:11Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Language models](), [Local AI]() Tags: [diffusion](), [gemma](), [inference](), [latency](), [local](), [mixture-of-experts-moe](), [none](), [text-generation]() ### AI overview DiffusionGemma is an experimental Apache 2.0-licensed 26B MoE text-diffusion model that generates text blocks in parallel for up to 4x faster GPU generation. It targets speed-critical local interactive workflows, while standard Gemma 4 remains recommended for maximum output quality. ### Source excerpt An overview of DiffusionGemma, an exceptionally fast text generation model with up to 4x faster speeds. ## Use Your Mac for AI Agents: Self-Host Gemma 4 12 B with Pulumi and Tailscale DevFeed: [Use Your Mac for AI Agents: Self-Host Gemma 4 12 B with Pulumi and Tailscale]() Original publisher: [Read original article]() Author: Pablo Seibelt Published: 2026-06-04T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [gemma4](), [llama.cpp](), [Local AI](), [Infrastructure as code](), [macOS](), [On-device AI](), [multimodal]() Tags: [ai](), [download](), [gemma-4](), [gemma4](), [gpu](), [infrastructure-as-code](), [kubernetes](), [llama-cpp](), [macos](), [multimodal](), [python](), [tailscale](), [tutorials]() ### AI overview A tutorial for self-hosting Gemma 4 12 B on a modern Mac using llama.cpp with Apple Metal acceleration. It combines host-native inference with a local Kubernetes cluster, Pulumi infrastructure as code, and Tailscale for secure access, and reports validation results on a MacBook Pro with an Apple M3 Max and 36 GB RAM. ### Source excerpt If you run AI tools and agents, you've probably accepted three tradeoffs: your data leaves your network, you can't work offline, and your bill scales with usage. Open-weight models now run well on consumer hardware. Once the model is on your machine, your data stays local, inference works offline, and tokens cost nothing. If you own a modern Mac, you can run a high-quality model yourself. Gemma 4 is an open-weights model family from Google. This post focuses on Gemma 4 12 B, released in June 2026, using Unsloth's Q8_0 GGUF. The 12 B model fits comfortably on a modern Mac while leaving enough headroom for local llama.cpp and a chat UI. We'll use llama.cpp for host-native inference, k3d for a local Kubernetes cluster, Pulumi for infrastructure as code, and Tailscale for secure access. Prerequisites This setup was validated on the following hardware: macOS 26 Tahoe, version 26.5 MacBook Pro with Apple M3 Max 36 GB RAM On this machine, llama.cpp reported about 20 output tokens per second for a 160-token validation response with unsloth/gemma-4-12b-it-GGUF, gemma-4-12b-it-Q8_0.gguf, and a 131,072-token context. Sustained throughput varies by prompt length, thermal state, and llama.cpp settings. You'll need brew, docker, pulumi, and tailscale installed. We'll also install k3d during the process. Run Gemma 4 with host-native llama.cpp We use llama.cpp directly on macOS to leverage Apple Metal acceleration. Running the LLM on the host is more efficient than trying to pass GPU access into a local Kubernetes VM. Install the build tools: brew install cmake git Then build llama.cpp from source and download the multimodal projector. In validation, Homebrew llama.cpp 9430 could run text inference, but it could not load the new Gemma 4 12 B projector and failed with unknown projector type: gemma4uv. Building current llama.cpp from source fixed that. llm_home="$HOME/pulumi-gemma4-llm" mkdir -p "$llm_home/models" "$llm_home/logs" if [ ! -d "$llm_home/llama.cpp/.git" ]; then git clon ## Holo3.1: Fast & Local Computer Use Agents DevFeed: [Holo3.1: Fast & Local Computer Use Agents]() Original publisher: [Read original article]() 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]() Topics: [computer-use](), [On-device AI](), [Local AI](), [Deployment](), [model-deployment](), [qwen](), [Benchmark](), [Cloud](), [NVFP4](), [browser]() Tags: [agent](), [agents](), [benchmark](), [benchmarks](), [browser](), [cloud](), [computer-use](), [deployment](), [devices](), [inference](), [json](), [model](), [models](), [nvfp4](), [on-device](), [performance](), [qwen](), [software]() ### 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. ## Distributing LLM inference in DwarfStar DevFeed: [Distributing LLM inference in DwarfStar]() Original publisher: [Read original article]() Published: 2026-05-25T14:54:59Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Large Language Model](), [Local AI](), [Hardware](), [quantization](), [GPU](), [Nvidia](), [DGX Spark]() Tags: [dgx-spark](), [gpu](), [hardware](), [inference](), [llm](), [local](), [money](), [nvidia](), [performance](), [quantization]() ### AI overview The article discusses the cost and performance trade-offs of running large language models locally. It compares high-end NVIDIA systems, DGX Spark, and Apple hardware, and argues that an M5 Max laptop with 128GB of memory may currently offer the most practical option for local inference. ### Source excerpt High end NVIDIA cards, and the server and power needed to run them, cost a lot of money, especially if you plan to reach enough VRAM to run massive models. The alternative, so far, has been Apple hardware, or the DGX Spark that, even if severely limited because of memory bandwidth, still allows to run LLMs prompt processing (prefill) fast enough. The Mac Studio provided up to 512GB unified memory, a solution with modest memory bandwidth (but much better than the Spark) and compute at a price that was, after all, given the current situation, relatively fair. For instance, with DwarfStar the Mac Studio M3 Ultra 512GB can run DeepSeek v4 PRO at 150 t/s prefill and ~10-13 t/s decoding, not great but at a level that is usable for certain use cases. Even 2-bit quantized, DeepSeek v4 PRO resists very well, like Flash at the same quantization (today I made PRO write a C compiler, I'll publish the video soon). I would not consider a trivial fact to run a frontier model at home, with a ~12k total spending. One could expect this to get better and better, but the situation at the horizon appears cloudy. There is almost zero hope that NVIDIA setups will get less expensive, and even a small company can't afford to easily purchase and handle a small data center for local inference. At the same time the RAM shortage is making it not exactly likely that we will see a Mac Studio with an M5 Ultra, maybe 1.2T/s memory bandwidth and more compute (the M5 Max is already faster, compute wise, and has the Neural Accelerators inside each GPU core that help with certain models). So the current situation for local inference is that the best machine is probably a laptop. The M5 Max 128GB can run DeepSeek v4 Flash and Mimo V2.5, 2-bit quantized, at very decent prefill and decoding speeds. We are talking of ~500 t/s prefill and ~35-40t/s decoding speed, with a performance slope as the context size increases which is very acceptable. At the cost of 6-7k depending on the configuration, this is curr ## Alternatives for the EDIT tool of LLM agents DevFeed: [Alternatives for the EDIT tool of LLM agents]() Original publisher: [Read original article]() Published: 2026-05-19T07:26:03Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Tool](), [Local AI](), [Large Language Model]() Tags: [agents](), [alternatives](), [inference](), [llm](), [local-llm](), [tool]() ### AI overview The article proposes a tag-based EDIT tool for LLM agents that preserves check-and-set semantics while reducing the tokens needed to repeat old text. It uses short line checksums alongside line numbers so edits can be validated without reproducing the original content. ### Source excerpt EDIT: of course this was already done in the past! I had little doubts but people just confirmed me about it on Twitter :) But, keep reading: the CRC32 compromise at the end is an interesting tradeoff, and this is a good discussion to have in general. Right now I'm working to an agent for my DS4 project. Local inference is token-poor, it's a battlefield where optimizations count. I was quite surprised by the fact the EDIT tool everybody is using right now forces the LLM to emit the old version of the text verbatim. This CAS (check and set) mode of operation, where I say EDIT old="foo" new="bar", is needed because there are often colliding edits (the user is editing as well, or checked out a different branch, and so forth) and because the LLM can just hallucinate that a given line had a given content. This means, basically, that just using line numbers is very fragile: to say, change line 22 with new="foobar" is not good. Yet I don't want my local LLM to throw away tokens rewriting the old text each time, also because certain times the old text has a lot of special chars and spaces that the model may get wrong; in this case the tool would fail, forcing the LLM to do the same edit again. So I (re)designed a tag-based EDIT tool that is still CAS style, but more tokens efficient. The READ and SEARCH tools return something like that: 10:Q8fA int count = 10; 11:rA3_ if (count > limit) { 12:Kq9z count = limit; 13:PX0b } So there are line numbers and tags. The tag is 4 chars, on average 2.5 LLM tokens, representing a checksum of the line. Now the LLM can edit like this: { "tool": "edit", "path": "/tmp/example.c", "line": 10, "tag": "Q8fA", "new": "int count = 11;" } Or, multi line, like this: { "tool": "edit", "path": "/tmp/example.c", "lines": "11:rA3_\n12:Kq9z\n13:PX0b", "new": "if (count > limit)\n return limit;" } The saving is significant especially when the agent is deleting big amounts of text, but also in the general case. However, there is some overhead due to the [Next page]()