# gemma4 Published articles for gemma4. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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 ## pyghidra-mcp Meets Ghidra GUI: Drive Project-Wide RE with Local AI DevFeed: [pyghidra-mcp Meets Ghidra GUI: Drive Project-Wide RE with Local AI]() Original publisher: [Read original article]() Author: clearbluejar Published: 2026-05-05T07:00:00Z Content type: article Language: en Sources: [clearbluejar]() Topics: [Ghidra](), [Reverse Engineering](), [Local AI](), [MCP Server](), [Artificial Intelligence](), [Processes]() Tags: [ai](), [gemma4](), [ghidra](), [local](), [local-ai](), [mcp](), [mcp-server](), [reverse-engineering]() ### AI overview The article presents pyghidra-mcp v0.2.0, which adds a GUI-backed mode allowing a local LLM to drive a live Ghidra CodeBrowser across an entire project. It demonstrates real-time function renaming, plate comments, and cross-binary analysis, with edits recorded in Ghidra's undo history. ### Source excerpt pyghidra-mcp v0.2.0 ships a GUI-backed mode that lets a local LLM drive a live Ghidra CodeBrowser at full project scope. Renames, plate comments, and cross-binary pivots land in real time, with every edit tagged in Ghidra's undo history while the session is alive. ## Welcome Gemma 4: Frontier multimodal intelligence on device DevFeed: [Welcome Gemma 4: Frontier multimodal intelligence on device]() Original publisher: [Read original article]() Author: merve; Pedro Cuenca; Sergio Paniego; ben burtenshaw; Steven Zheng; Alvaro Bartolome; Nathan Habib Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gemma4](), [multimodal](), [Open Source](), [long-context](), [quantization](), [llama.cpp](), [MLX](), [Rust](), [webgpu]() Tags: [audio](), [benchmarks](), [fine-tuning](), [gemma](), [gemma4](), [generation](), [google](), [guide](), [llama](), [llama-cpp](), [local](), [long-context](), [mlx](), [models](), [moe](), [multimodal](), [on-device](), [open](), [open-source](), [quantization](), [science](), [speed]() ### AI overview Gemma 4 is presented as an open, Apache 2-licensed family of multimodal models supporting image, video, text, and, for selected variants, audio inputs. The article highlights on-device deployment, long-context support, compatibility across libraries and devices, quantization, benchmark results, and implementation options including llama.cpp, MLX, WebGPU, and Rust. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.