Google’s Gemma 12B AI: Local PC Performance and Capabilities
Generated: 2026-06-13 · API: Gemini 2.5 Flash · Modes: Summary
Google’s Gemma 12B AI: Local PC Performance and Capabilities
Clip title: Google’s New FREE AI Model Can Run on Your PC! Author / channel: Gary Explains URL: https://www.youtube.com/watch?v=MVd-81QOGkw
Summary
This video introduces Google’s new 12 billion parameter version of Gemma 4, a large language model designed to run locally on personal computers. The presenter highlights this new variant’s significance as it fills a crucial gap between the previously released smaller (2.3B and 4.5B effective parameters) and larger (26B Mixture-of-Experts and 31B dense) models. The primary advantage emphasized is its ability to operate efficiently on accessible hardware, specifically PCs with as little as 16GB of RAM, and even on a Raspberry Pi 5.
Detailing the hardware requirements, the 12B parameter model has a file size of 7.6GB and optimally utilizes approximately 10GB of RAM or VRAM, making it well-suited for typical enthusiast-level systems. In terms of performance, the 12B model achieves around 110 tokens per second when run on an RTX 5090 GPU, positioning it as an “efficient” option. While faster than the largest 31B model (60 tok/s), it is notably slower than the smaller, less capable versions (278 tok/s for 2.3B, 193 tok/s for 4.5B, and 183 tok/s for the 26B MoE). The video also demonstrates its capability on less powerful hardware, such as an Intel i7-1195G7 CPU (4 tok/s) and a Raspberry Pi 5 (1.25 tok/s).
The presenter further showcases the model’s capabilities through various logical reasoning and factual recall tests. The 12B parameter model successfully solves simple “Alice” questions and the more complex “Cookies” mathematical problem, areas where the smaller Gemma4 versions often fail. For the challenging “Hourglass 7 & 11” logic puzzle, the 12B model mostly provides correct answers, occasionally making errors, which the presenter attributes to operating at the very edge of its capabilities in its 4-bit quantized form. Impressively, when asked to “tell me about Paris in 1883” without internet access, the model generated a 700-word essay with largely accurate historical details, though it did make one minor factual error regarding a writer’s activity timeline. A similar query about “New York in 1901” also yielded a comprehensive and well-structured response.
In conclusion, the Gemma 4’s new 12 billion parameter version represents a compelling middle-ground option for those looking to run powerful AI models locally. It offers a significant leap in capability over its smaller siblings while remaining far more accessible in terms of hardware requirements than its larger counterparts. This balance of reasonable resource usage and strong performance in logical reasoning and knowledge generation makes it an excellent choice for a wider range of users and local AI applications.
Video Description & Links
Description
Google has released a 12 billion parameter variant of its Gemma 4 series. The model only needs 16GB of RAM, or better still 16GB of VRAM, for a fast and capable model.
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Tags
Gary Explains, Tech, Explanation, Tutorial, Google, Gemma 4, Gemma 4 12b, AI, Local AI, Local LLM, Ollama, LM Studio