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Gemma 4 12B local setup thread gathers user hardware and performance data

Google's Gemma 4 12B multimodal model is now available, with the community quickly releasing various quantized versions for local setup. A Reddit thread on r/MachineLearning is collecting user experiences regarding hardware requirements, quantization methods, and performance metrics like tokens per second. Users are sharing details on their setups, including chip, RAM, GPU, runtime environments, and practical use cases, to determine the model's actual performance floor on consumer hardware. AI

IMPACT Community-driven data collection will help users assess Gemma 4 12B's viability on local hardware.

RANK_REASON Community discussion thread about setting up and evaluating an open-source model release. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/MachineLearning →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Individual_Soil4641 ·

    Gemma 4 12B local setup thread — what's your hardware, quant, and use case? [D]

    <!-- SC_OFF --><div class="md"><p>ok so the model's been up on HF now (apache 2.0, ~12B BF16, any-to-any multimodal). community has already shipped a pile of quants:</p> <p>- GGUF: unsloth, bartowski, ggml-org, lmstudio-community</p> <p>- MLX: mlx-community has 4bit / 8bit / bf16…