AMD just went Day Zero on Gemma 4 🔥

Every GPU. Every CPU. Every major AI tool, ready NOW.

Most people don't know what this means for local AI on your PC.

Read this 👇
https://geekrealmhub.com/amd-gemma-4-support-gpus-cpus/

#AMD #AI #LocalAI

ΩWNÆTHER- The Future of Enterprise AI Is Here: Introducing the ΩWNÆTHER Enterprise AI Operating System…

https://medium.com/@ownaether/the-future-of-enterprise-ai-is-here-introducing-the-%CF%89wn%C3%A6ther-enterprise-ai-operating-system-27367a3726a2

#AI #EnterpriseAI #BusinessAI #AgenticAI #AIOS #AIApps #LLMs #AIModels #AIInfrastructure #AIOrchestration #AIAgents #AIConstellations #AIMarketplaces #AITools #AICompute #LocalAI #AIDevices #AIHardware #AIServices #AINetworks #AIPlatforms #AIEconomies #AIEcosystems #A2A #MCP #RAG #AIBlockchain #DePin #AITokens #X402 #MPP #PreLaunch

Benchmarking Gemma 4 (e4b): Linux vs. Mac 🚀 .

I tested the e4b gemma 4 variant on a 32GB Linux setup vs. a 16GB Mac.
The Mac was 4.5x faster (44s vs 199s) and nailed a complex poem constraint.
Find more details about _why_ Linux results were different on
https://www.lotharschulz.info/2026/04/04/gemma-4-performance-showdown-linux-vs-mac-benchmarks/
Also, experimented with Ollama MLX preview support using a qwen model.

#Gemma4 #AI #LocalAI #Linux #AppleSilicon

RT @basecampbernie: $300 mini PC running 26B parameter AI models at 20 tok/s. Minisforum UM790 Pro ($351) + AMD Radeon 780M iGPU + 48GB DDR5-5600 + 1TB NVMe. The secret: the 780M has no dedicated VRAM. It shares your DDR5 via unified memory. The BIOS says "4GB VRAM" but Vulkan sees the full pool. I'm allocating 21+ GB for model weights on a GPU with "4GB VRAM." The iGPU reads weights directly from system RAM at DDR5 bandwidth (~75 GB/s). MoE only activates 4B params per token = 2-4 GB of reads. That's why 20 tok/s works. What it runs: - Gemma 4 26B MoE: 19.5 tok/s, 110 tok/s prefill, 196K context - Gemma 4 E4B: 21.7 tok/s faster than some RTX setups - Qwen3.5-35B-A3B: 20.8 tok/s - Nemotron Cascade 2: 24.8 tok/s Dense 31B? 4 tok/s, reads all 18GB per token, bandwidth wall. MoE same quality? 20 tok/s. Full agentic workflows via @NousResearch Hermes agent with terminal, file ops, web, 40+ tools, all against local models. No API keys. Just a box on your desk. The RAM is the pain right now. DDR5 prices 3-4x what they were a year ago. But the compute is free forever after you buy it. @Hi_MINISFORUM @ggerganov llama.cpp + Vulkan + @UnslothAI GGUFs + @AMDRadeon RDNA 3. Fits in your hand. #LocalLLM #Gemma4 #llama_cpp #AMD #Radeon780M #MoE #LocalAI #AI #OpenSource #GGUF #HermesAgent #NousResearch #DDR5 #MiniPC #EdgeAI #UnifiedMemory #Vulkan #iGPU #RunItLocal #AIonDevice

Mehr auf Arint.info

#agent #API #GGUF #llama #LocalAI #OpenSource #Qwen3535 #arint_info

https://x.com/basecampbernie/status/2040326984446935059#m

💡 Insight krusial yang harus kamu baca hari ini.

"How to Run Local AI Agents: A Comprehensive Guide"

🔗 Akses repositori/dokumentasi: https://www.authorsvoice.net/kurasi-beracun-menguliti-kartel-data-di-balik-layar-2026/

#ai #localai #llm

Tested Cogito V1 8B on my Linux server. 83 t/s, 5.4GB VRAM, 131k context. The real story is where it deliberately wrote worse code because it decided a beginner needed simplicity over efficiency -- and admitted it! That's IDA self-reflection making a live call.
I guess a 5GB model with a conscience is worth more than a 70B model with none?

Read the full breakdown below.

#LocalAI #Ollama #HomeLabAI #LLM #AIBenchmark

https://goarcherdynamics.com/2026/04/03/aihome-cogito-v1-8b-review/?utm_source=mastodon&utm_medium=jetpack_social

🚀 New AI Battle: Gemma 4 on Linux! 🐧

I tested the new Gemma 4 (e4b) running locally via Ollama on Linux. How does it solve the "HORSE-EARTH" poem test?

🎭 Linguistics Grade: B
Gemma 4 nailed the complex acrostic/telestich constraints but had to invent a new word—"gleama"—to make the rhyme work. A "beautiful mess" that shows real creative grit.

All technical details: https://www.lotharschulz.info/2026/04/03/gemma-4-on-linux/

#Gemma4 #Linux #Ollama #OpenSource #AI #MachineLearning #LocalAI #SelfHosted

[marmonitor - tmux 상태바에서 AI 코딩 에이전트 세션을 실시간 추적

marmonitor는 tmux 상태바에 AI 코딩 에이전트 세션의 실시간 상태를 표시하여 pane 전환 없이 세션 현황(phase, 세션 수, 토큰 사용량, CPU/MEM 등)을 확인할 수 있도록 돕는 오픈소스 도구이다. TypeScript로 작성된 이 도구는 로컬 프로세스 정보를 읽기 전용으로 관찰하며, API 키나 네트워크 통신 없이 작동한다. macOS를 우선 지원하며, 다중 AI 에이전트 작업 시 유용한 도구로 평가받고 있다. 관련 오픈소스 도구로는 `agent-of-empires`, `devglow`, `so-agentbar` 등이 언급되었다.

https://news.hada.io/topic?id=28159

#aiagents #tmux #devtool #monitoring #localai

New update for the slides of my talk "Run LLMs Locally": WebGPU

Now models can run completely inside the browser using Transformers.js, Vulkan and WebGPU (slower than llama.cpp, but already usable).

https://codeberg.org/thbley/talks/raw/branch/main/Run_LLMs_Locally_2026_ThomasBley.pdf

#ai #llm #llamacpp #stablediffusion #gptoss #qwen3 #glm #localai #webgpu

don't expect llm generated code to be correct ↓