My local LLM rewrote my resume better than ChatGPT, and it's not even close
…Simultaneously, I uploaded my resume to my local LLM, which runs the Gemma 4 model through Ollama . When I have plenty of time on my hands, I usually prefer using the Quen…
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Google Gemma
Gemma is a family of open-weight language models released by Google for text generation and related NLP tasks.
Google Gemma 4 in your pocket: How to run the latest AI fully offline
…us a commission. Learn more. General technology AI Google Gemma 4 in your pocket: How to run the latest AI fully offline Google's new AI Edge Gallery brings local Gemma 4…
Google's latest trick gets Gemma 4 running 3x faster right on your phone
…This allows the model to use the memory and the compute more efficiently. Google’s recently launched Gemma 4 edge AI models are especially designed to run locally on consumer-hosted hardware…
Google's Gemma 4 isn't the smartest local LLM I've run, but it's the one I reach for most
…Most people who have used local models have probably dealt with the experience of open-weight models struggling to call tools consistently and effectively. Gemma 4 handles it reliably, as Google baked…
Top stories
Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM
3d agoI ran Gemma 4 and Qwen 3.5 for the same local tasks, and one pulled miles ahead
3d agoI put Google's Gemma 4 on my homelab and Tailscale on my phone — and cancelled Claude Pro
1w agoGoogle's free Gemma 4 model runs on hardware you probably already own
1w agoI tested 3 local LLMs on my actual work — and each model won at something different
…Gemma 4 is also one of the top models recommended for mobile use for this reason. Related Google's Gemma 4 isn't the smartest local LLM I've run, but it…
I stopped hitting Claude's message limit by building a local AI pipeline that does the heavy lifting
…Related I replaced the expensive Claude Pro subscription with these local models, and my productivity didn’t drop a bit Local-first vibe coding Gemma 4 writes the code And Claude makes…
I turned my phone into a local LLM server, and it handles vision, voice, and tool calls
…The phone is doing all the thinking All local using Termux Setup on the phone itself is surprisingly straightforward once you know what you're doing. I installed Termux from F-Droid…
Discussions and forums
Using a Gemma4 Safetensor Already Downloaded Locally
Hi everyone. I need some help or advice. I’m learning how to use N8N, so I downloaded Docker and installed N8N locally. I also wanted to install Gemma4, which I use in ComfyUI to help with image generation prompts. Is it…
Qwen 3.6 27B vs Gemma 4 31B - making Packman game!
Gemma just crushed Qwen in a local LLM gamedev contest! Device: MacBook Pro M5 Max, 64GB RAM Qwen 3.6 27B: 32 tokens/sec · 18m 04s · 33,946 tokens. Gemma 4 31B: 27 tokens/sec · 3m 51s · 6,209 tokens. So what is more impo…
Show HN: Hitoku Draft – Context aware local assistant
Hi guys.I have been working on Hitoku Draft, an open-source, voice-first AI assistant that runs entirely locally. I posted about it already, and now it has also transcription with voice editing. Looking for feedback, as …
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Show HN: Charm – on-device spelling, grammar, and prediction for macOS
I've spent the last year building Charm, a native macOS menu bar app that corrects spelling, fixes grammar, and predicts your next word.Three features:- Spells: NSSpellChecker plus a local LLM for context-aware correctio…
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Multi-Token Prediction (MTP) for LLaMA.cpp - Gemma 4 speedup by 40%
Implemented Multi-Token Prediction for LLaMA.cpp. Quantized Gemma 4 assistant models into GGUF format. Ran tests on a MacBook Pro M5Max. Gemma 26B with MTP drafts tokens 40% faster. Prompt: Write a Python program to find…
Lemonade 10.1 Released For Latest Improvements For Local LLMs On AMD GPUs & NPUs
…Linux useful for running large language models (LLMs) where as before the Linux build could only target GPUs, released on Monday was Lemonade 10.1 with more enhancements to this local LLM…
A Modder Repurposed a Used V100 For LLM Acceleration
…In another test using Gemma 4 E4B, it managed 108 tokens per second, compared to the RTX 3060's mere 76 tokens per second. It needed nearly 200W of power to do…
I ran local AI models on a six-year-old laptop with no GPU, and they actually worked
…But when it comes to quick factual questions, simple rephrasing, or just getting a feel for how local inference behaves on your tiny machine, it's genuinely useful and almost absurdly light…