Gemma 4 Local Hardware Matcher
Auto-detects your hardware to recommend the best Gemma 4
Gemma 4 Local Hardware Matcher launched on April 10, 2026.
What it does
Stop guessing VRAM requirements. Run Gemma 4 local with auto GPU detection — get the exact model, quantization, and run command for your PC, Mac, iPhone, or Android.
Complete Gemma 4 hardware requirements guide. Compare E2B, E4B, 26B MoE and 31B Dense models — minimum RAM, VRAM, recommended GPU, and step-by-step setup for PC, Mac, and mobile.
Quick reference for every Gemma 4 model tier and popular hardware configuration. No installs, no sign-ups. Open the page and get a personalized setup in seconds. The moment you load this page, the tool reads your GPU via browser-native WebGPU and WebGL APIs. On Mac, it identifies your exact Apple Silicon chip and unified memory size. On PC, it reads your NVIDIA / AMD model and VRAM. No data leaves your browser. Your detected GPU, VRAM (or unified memory), and OS are cross-referenced against Gemma 4's model tiers — Edge for phones, 26B MoE for consumer hardware, 31B Dense for workstations. The matcher picks the largest model your hardware can run comfortably and flags any known caveats. You…from gemmamatch.com
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Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series MacJul 2026 · github.com · ▲919Hi HN, I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called TurboFieldfare and is written in Swift and Metal. I have always adored on-device AI. It feels like magic that you can run a powerful NN on your Mac or iPhone. So I wanted to push the limits a bit and run a model whose weights don’t fit in memory. The model’s 4-bit quantized weights occupy roughly 14 GB, which makes running it with conventional inference tools almost impossible on an 8 GB or even 16 GB Mac once the OS, applications, and KV cache are…
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Gemma 4 Multimodal Fine-Tuner for Apple SiliconApr 2026 · github.com · ▲235About six months ago, I started working on a project to fine-tune Whisper locally on my M2 Ultra Mac Studio with a limited compute budget. I got into it. The problem I had at the time was I had 15,000 hours of audio data in Google Cloud Storage, and there was no way I could fit all the audio onto my local machine, so I built a system to stream data from my GCS to my machine during training. Gemma 3n came out, so I added that. Kinda went nuts, tbh. Then I put it on the shelf. When Gemma 4 came out a few days ago, I dusted it off, cleaned it up, broke out the Gemma part from the Whisper…
I run a vision model on every screenshot, locally, on a 4GB GPUJun 2026 · github.com · ▲36AI-powered screen memory — captures, analyzes, and lets you search/chat your screen history. Powered by Gemma 4 . 100% local, 100% private. - ayushh0110/ScreenMind
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