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Stop paying for every API call. Download once and run forever. My 1-post “thread” on Gemma. → Gemma is Google’s open weights model family. → Gemma runs locally on laptop and phone. → Gemma 3N is optimized for low RAM devices. → Variants exist for medical and safety and... show more

Julian Goldie SEO

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14,403 views • 7 months ago •via X (Twitter)

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Stop paying for “knowledge base” tools. Build your own NotebookLM clone locally for free. Here’s the setup flow → → Install Docker (one time). → Install Ollama. → Pull Gemma 3 1B for the chat model. → Add mxbai-embed-large for embeddings (semantic search). → Run Open NotebookLM via Docker. → Select Chat Model + Tools Model or it won’t work. Save this video, you’ll build your private research hub in 10 minutes. Want the SOP? DM me. 💬

Stop paying for “knowledge base” tools. Build your own NotebookLM clone locally for free. Here’s the setup flow → → Install Docker (one time). → Install Ollama. → Pull Gemma 3 1B for the chat model. → Add mxbai-embed-large for embeddings (semantic search). → Run Open NotebookLM via Docker. → Select Chat Model + Tools Model or it won’t work. Save this video, you’ll build your private research hub in 10 minutes. Want the SOP? DM me. 💬

Julian Goldie SEO

40,659 views • 6 months ago

omg… Google just dropped a model that can run locally on your phone. It's called Gemma 3n and it's open-weight, multimodal, and needs just 2GB RAM. Why is no one freaking out? Here’s why it matters:

omg… Google just dropped a model that can run locally on your phone. It's called Gemma 3n and it's open-weight, multimodal, and needs just 2GB RAM. Why is no one freaking out? Here’s why it matters:

Louis Gleeson

34,954 views • 1 year ago

You can run OpenClaw free now. Most people still think agents require subscriptions. Here’s the easiest setup: → download Atomic Chat → install OpenClaw in one click → activate Gemma 4B locally → switch models anytime → run agents without API costs This changes agent accessibility completely. Save this video, you’ll run OpenClaw without paying monthly. Want the SOP? DM me. 💬

You can run OpenClaw free now. Most people still think agents require subscriptions. Here’s the easiest setup: → download Atomic Chat → install OpenClaw in one click → activate Gemma 4B locally → switch models anytime → run agents without API costs This changes agent accessibility completely. Save this video, you’ll run OpenClaw without paying monthly. Want the SOP? DM me. 💬

Julian Goldie SEO

15,054 views • 3 months ago

Today, we’re launching Gemma 4, our most intelligent open models to date. Built with the same breakthrough technology as Gemini 3, Gemma 4 brings advanced reasoning to your personal hardware and devices. Here’s what Gemma 4 unlocks for developers: — Intelligence-per-parameter: Our 31B (Dense) and 26B (MoE) models deliver state-of-the-art performance for their size, outcompeting models 20x their size on Arena.ai — Commercial flexibility: Released under a permissive Apache 2.0 license for complete developer flexibility and digital sovereignty — Agentic workflows: Native support for function-calling and structured JSON output allows you to build reliable, autonomous agents — Multimodal edge AI: The E2B and E4B models bring native vision, audio, and low latency to mobile and IoT devices — Long-context reasoning: Up to 256K context windows allow you to process entire repositories or large documents in a single prompt Whether you're building global applications in 140+ languages or local-first AI code assistants, Gemma 4 is built to be your foundation. Explore in Google AI Studio or download the weights on Hugging Face, Kaggle, and ollama.

Today, we’re launching Gemma 4, our most intelligent open models to date. Built with the same breakthrough technology as Gemini 3, Gemma 4 brings advanced reasoning to your personal hardware and devices. Here’s what Gemma 4 unlocks for developers: — Intelligence-per-parameter: Our 31B (Dense) and 26B (MoE) models deliver state-of-the-art performance for their size, outcompeting models 20x their size on Arena.ai — Commercial flexibility: Released under a permissive Apache 2.0 license for complete developer flexibility and digital sovereignty — Agentic workflows: Native support for function-calling and structured JSON output allows you to build reliable, autonomous agents — Multimodal edge AI: The E2B and E4B models bring native vision, audio, and low latency to mobile and IoT devices — Long-context reasoning: Up to 256K context windows allow you to process entire repositories or large documents in a single prompt Whether you're building global applications in 140+ languages or local-first AI code assistants, Gemma 4 is built to be your foundation. Explore in Google AI Studio or download the weights on Hugging Face, Kaggle, and ollama.

Google AI

1,669,327 views • 3 months ago

Microsoft just made expensive GPUs useless. And it runs on your 5-year-old laptop. BitNet B1.58 crushes models 10x bigger using 96% less energy. Here's what changed: → Uses ternary weights (only -1, 0, +1) instead of full precision floats → 0.4GB memory usage (runs on a phone, no GPU needed) → 29 milliseconds per response (real-time fast) → Beats Llama 3 and Gemma on benchmarks while using almost no power → Free on HuggingFace, MIT license (download and run today) No API costs. No cloud bills. No data leaving your machine. ChatGPT charges per token. This is free forever. Save this video. Want the SOP? DM me. 💬

Microsoft just made expensive GPUs useless. And it runs on your 5-year-old laptop. BitNet B1.58 crushes models 10x bigger using 96% less energy. Here's what changed: → Uses ternary weights (only -1, 0, +1) instead of full precision floats → 0.4GB memory usage (runs on a phone, no GPU needed) → 29 milliseconds per response (real-time fast) → Beats Llama 3 and Gemma on benchmarks while using almost no power → Free on HuggingFace, MIT license (download and run today) No API costs. No cloud bills. No data leaving your machine. ChatGPT charges per token. This is free forever. Save this video. Want the SOP? DM me. 💬

Julian Goldie SEO

76,615 views • 6 months ago

Claude Code is now completely free forever 🤯 Just connect it to Ollama and run a local model like Google’s new Gemma 4. That means Claude Code agents can run directly on your computer with zero API bills and zero subscriptions. Google may have just ended AI subscription fees for good. 🚀

Claude Code is now completely free forever 🤯 Just connect it to Ollama and run a local model like Google’s new Gemma 4. That means Claude Code agents can run directly on your computer with zero API bills and zero subscriptions. Google may have just ended AI subscription fees for good. 🚀

Julian Goldie SEO

10,098 views • 3 months ago

OMG… you can now fine-tune your own AI model and run it entirely on your device. No servers. No API keys. No data leaks. Gemma 3 270M just made local LLMs real and it only takes about an hour to build your own. Here’s how:

OMG… you can now fine-tune your own AI model and run it entirely on your device. No servers. No API keys. No data leaks. Gemma 3 270M just made local LLMs real and it only takes about an hour to build your own. Here’s how:

Louis Gleeson

79,508 views • 9 months ago

🗓️ Release Week Recap Big week. mlx-audio and mlx-vlm are now among some of the fastest-growing OSS projects. Here’s what we shipped last week. Gemma 4 on Apple Silicon Two awesome releases by our partner Google DeepMind & Google Gemma : > Gemma 4 12B — their new dense, unified multimodal model. It uses an encoder free audio path and simplified vision encoder. > Gemma 4 QAT — quantization-aware training checkpoints, optimized to run locally on consumer GPUs and edge devices, compressing the model while preserving the quality you expect from Gemma 4. On the audio 🎧 side we added support for 15+ new TTS, ASR & VAD models, faster long-form transcription, and an expanded OpenAI-compatible audio server. All local on Apple Silicon. Huge thanks to every contributor and my co-maintainer Lucas Newman. 🙏🏽

🗓️ Release Week Recap Big week. mlx-audio and mlx-vlm are now among some of the fastest-growing OSS projects. Here’s what we shipped last week. Gemma 4 on Apple Silicon Two awesome releases by our partner Google DeepMind & Google Gemma : > Gemma 4 12B — their new dense, unified multimodal model. It uses an encoder free audio path and simplified vision encoder. > Gemma 4 QAT — quantization-aware training checkpoints, optimized to run locally on consumer GPUs and edge devices, compressing the model while preserving the quality you expect from Gemma 4. On the audio 🎧 side we added support for 15+ new TTS, ASR & VAD models, faster long-form transcription, and an expanded OpenAI-compatible audio server. All local on Apple Silicon. Huge thanks to every contributor and my co-maintainer Lucas Newman. 🙏🏽

Prince Canuma

11,731 views • 1 month ago

Gemma 3 + Ollama + N8N + Open Router 🔥 What a model! Great release Google! 👏🏻 - Runs locally via Ollama - Outperforms Claude Mini & DeepSeek - Available in 1B, 4B, 12B & 27B sizes - 128k context window - Free API access Save this video it will save you hundreds on AI costs. 🚀 Want the SOP? DM me.

Gemma 3 + Ollama + N8N + Open Router 🔥 What a model! Great release Google! 👏🏻 - Runs locally via Ollama - Outperforms Claude Mini & DeepSeek - Available in 1B, 4B, 12B & 27B sizes - 128k context window - Free API access Save this video it will save you hundreds on AI costs. 🚀 Want the SOP? DM me.

Julian Goldie SEO

140,500 views • 1 year ago

WHY IS NO ONE TALKING ABOUT THIS?? Gemma 3n model was one of the best surprises for me. The fact that you can run it on edge devices even with just 2GB of RAM is impressive. A few weeks back, I was on holiday and used the Gemini Live feature a lot. But I kept running into issues whenever I was in a place where the network wasn’t reliable. Gemma 3n : > Multimodal: Supports text and image inputs; video/audio in development. > Context Window: Up to 128K tokens (32K for 1B model). > Multilingual: Trained on 140+ languages. > Privacy: Offline, on-device processing for data security. > Model Sizes: E2B (5B parameters, 2GB RAM) and E4B (10–12B parameters, 3GB RAM). Video credit: Google YT

WHY IS NO ONE TALKING ABOUT THIS?? Gemma 3n model was one of the best surprises for me. The fact that you can run it on edge devices even with just 2GB of RAM is impressive. A few weeks back, I was on holiday and used the Gemini Live feature a lot. But I kept running into issues whenever I was in a place where the network wasn’t reliable. Gemma 3n : > Multimodal: Supports text and image inputs; video/audio in development. > Context Window: Up to 128K tokens (32K for 1B model). > Multilingual: Trained on 140+ languages. > Privacy: Offline, on-device processing for data security. > Model Sizes: E2B (5B parameters, 2GB RAM) and E4B (10–12B parameters, 3GB RAM). Video credit: Google YT

AshutoshShrivastava

210,490 views • 1 year ago

We've partnered to bring more Gemma 3 quantized models to you! 🚀 We worked with Georgi Gerganov llama.cpp, LM Studio, MLX, ollama to make sure you can run it using your favorite tool! Gemma models optimized with QAT, reduce memory requirements while keeping quality! All models checkpoints are available on Hugging Face and Kaggle. 🤗 What does this mean? - Gemma 3 27B (int4): Fits on NVIDIA RTX 3090 (24GB VRAM) or similar. - Gemma 3 12B (int4): Only needs a NVIDIA RTX 4060 (8GB VRAM) or similar. - Gemma 3 4B, 1B (int4): Run anything with more than 2.5GB Memory. Want to see it in action? Video below shows how easy it is to get started using LMStudio:

We've partnered to bring more Gemma 3 quantized models to you! 🚀 We worked with Georgi Gerganov llama.cpp, LM Studio, MLX, ollama to make sure you can run it using your favorite tool! Gemma models optimized with QAT, reduce memory requirements while keeping quality! All models checkpoints are available on Hugging Face and Kaggle. 🤗 What does this mean? - Gemma 3 27B (int4): Fits on NVIDIA RTX 3090 (24GB VRAM) or similar. - Gemma 3 12B (int4): Only needs a NVIDIA RTX 4060 (8GB VRAM) or similar. - Gemma 3 4B, 1B (int4): Run anything with more than 2.5GB Memory. Want to see it in action? Video below shows how easy it is to get started using LMStudio:

Philipp Schmid

15,996 views • 1 year ago

This video is at normal speed. Gemma 4 12B MLX version running locally at 50 tokens/sec. Thank you Google DeepMind team. This model feels really solid for a lot of small local tasks and everyday AI workflows.

This video is at normal speed. Gemma 4 12B MLX version running locally at 50 tokens/sec. Thank you Google DeepMind team. This model feels really solid for a lot of small local tasks and everyday AI workflows.

AshutoshShrivastava

19,822 views • 1 month ago

Hot tip for anyone doing AI dev: Use Ollama to easily run models like Deepseek-r1 or Gemma locally on your machine. It downloads them and spins up a server with an OpenAI SDK compatible API The smaller models are fast and good enough to work on new features or debug streaming without having to pay for API requests

Hot tip for anyone doing AI dev: Use Ollama to easily run models like Deepseek-r1 or Gemma locally on your machine. It downloads them and spins up a server with an OpenAI SDK compatible API The smaller models are fast and good enough to work on new features or debug streaming without having to pay for API requests

Wes Bos

153,435 views • 1 year ago

Google's new Gemma 4 is excellent, and the 26B MoE version is likely the best model to run on a 32GB Framework Desktop. It's fast, smart, and also great for tool calling if you use it with OpenClaw🦞 or other local agent platforms.

Google's new Gemma 4 is excellent, and the 26B MoE version is likely the best model to run on a 32GB Framework Desktop. It's fast, smart, and also great for tool calling if you use it with OpenClaw🦞 or other local agent platforms.

Framework

58,593 views • 3 months ago

Ex-NVIDIA engineer who built Unsloth explained RL, kernels, reasoning, quantization, and agents in 2 hours 42 minutes - better than $5000 fine-tuning bootcamps. pick the base model -> write triton kernels for 2x faster fine-tune -> quantize to 4-bit -> run GRPO/DPO -> ship a reasoning model on your single GPU. That loop is why Unsloth is the default way to fine-tune Llama, Qwen, Gemma, and Phi on hardware you already own. Unsloth + Triton kernels + 4-bit quantization + GRPO/DPO + single-GPU fine-tuning - that's the stack. Watch and save it, then fine-tune your first model tonight.

Ex-NVIDIA engineer who built Unsloth explained RL, kernels, reasoning, quantization, and agents in 2 hours 42 minutes - better than $5000 fine-tuning bootcamps. pick the base model -> write triton kernels for 2x faster fine-tune -> quantize to 4-bit -> run GRPO/DPO -> ship a reasoning model on your single GPU. That loop is why Unsloth is the default way to fine-tune Llama, Qwen, Gemma, and Phi on hardware you already own. Unsloth + Triton kernels + 4-bit quantization + GRPO/DPO + single-GPU fine-tuning - that's the stack. Watch and save it, then fine-tune your first model tonight.

h100envy

487,633 views • 10 days ago

Demis says he wants to see a Western open source AI stack and that we’re losing to China. He also says Google doesn’t have enough compute to build two frontier (open and closed) models, which is why Gemma is a smaller family of models. Watch this incredible clip. Shout out Y Combinator and Garry Tan for the fantastic interview.

Demis says he wants to see a Western open source AI stack and that we’re losing to China. He also says Google doesn’t have enough compute to build two frontier (open and closed) models, which is why Gemma is a smaller family of models. Watch this incredible clip. Shout out Y Combinator and Garry Tan for the fantastic interview.

Matthew Berman

296,424 views • 2 months ago

We're thrilled to announce SignGemma, our most capable model for translating sign language into spoken text. 🧏 This open model is coming to the Gemma model family later this year, opening up new possibilities for inclusive tech. Share your feedback and interest in early testing →

We're thrilled to announce SignGemma, our most capable model for translating sign language into spoken text. 🧏 This open model is coming to the Gemma model family later this year, opening up new possibilities for inclusive tech. Share your feedback and interest in early testing →

Google DeepMind

525,030 views • 1 year ago

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

atomic.chat

151,786 views • 1 month ago

Want a free private AI agent on your laptop? Here’s the simple setup. 👇 1. Install Hermes Agent from GitHub. 2. Install Ollama. 3. Download Gemma 4 26B. 4. Use Docker to keep everything contained. 5. Add Firecrawl so the agent can research the web. 6. Connect Telegram so you can chat with it from your phone. 7. Turn on context compression so it stays sharp. 8. Set the daily reset for 4 AM so it refreshes while you sleep. What you get at the end: → local AI agent → private workflow → web research built in → self improving skills → no monthly model rental That is a serious setup for anyone building with AI. Save this video, you’ll have the easiest beginner roadmap to follow later. Want the SOP? DM me. 💬

Want a free private AI agent on your laptop? Here’s the simple setup. 👇 1. Install Hermes Agent from GitHub. 2. Install Ollama. 3. Download Gemma 4 26B. 4. Use Docker to keep everything contained. 5. Add Firecrawl so the agent can research the web. 6. Connect Telegram so you can chat with it from your phone. 7. Turn on context compression so it stays sharp. 8. Set the daily reset for 4 AM so it refreshes while you sleep. What you get at the end: → local AI agent → private workflow → web research built in → self improving skills → no monthly model rental That is a serious setup for anyone building with AI. Save this video, you’ll have the easiest beginner roadmap to follow later. Want the SOP? DM me. 💬

Julian Goldie SEO

31,292 views • 3 months ago

OpenClaw + Qwen 3.5 just unlocked “Claude-level” local agents. No API bills, no cloud, no data leaving your machine. Here’s the setup flow. → Install the Ollama pre-release (needed for the newest Qwen build). → Pull Qwen 3.5 locally (it’s a big download). → Launch OpenClaw with the Qwen model. → Run one real workflow: publish, message, schedule. If you can run it locally, you own the whole stack. Save this video, you’ll set up a free private agent. Want the SOP? DM me. 💬

OpenClaw + Qwen 3.5 just unlocked “Claude-level” local agents. No API bills, no cloud, no data leaving your machine. Here’s the setup flow. → Install the Ollama pre-release (needed for the newest Qwen build). → Pull Qwen 3.5 locally (it’s a big download). → Launch OpenClaw with the Qwen model. → Run one real workflow: publish, message, schedule. If you can run it locally, you own the whole stack. Save this video, you’ll set up a free private agent. Want the SOP? DM me. 💬

Julian Goldie SEO

163,315 views • 4 months ago