7 posts
On a local development machine with an RTX 4060 Laptop GPU, 8GB VRAM, and 32GB memory assigned to WSL, I tested Qwen3 4B, Qwen3 8B, Gemma 4 E4B, and Gemma 4 12B for load time, generation speed, VRAM usage, multimodal capability, and practical trade-offs.
The real value of local open models is not a benchmark score, but whether they can enter tasks that happen every day. This article puts Qwen3 4B, Qwen3 8B, Gemma 4 E4B, and Gemma 4 12B into three workflows: development assistance, image understanding, and writing organization.
A practical test of gemma4:12b audio input on a local test machine: the model metadata declares audio support, but Ollama's native /api/chat path could not reliably accept audio; the working path was the OpenAI-compatible input_audio block.
A practical test from official docs to local verification: Gemma 4 12B can understand video as frame sequences, but Ollama did not accept MP4 directly in this test. The stable path is frame extraction plus multi-image input.
A practical test of Gemma 4 12B through Ollama: generating Canvas code, connecting it to OpenClaw, calling QVeris tools, and judging where a 12B local model is useful and where it still needs guardrails.
A short introduction to Google's lightweight Gemma LLM family, including Kaggle resources, common model formats, 2B and 7B variants, and basic hardware expectations.
A simple local Gemma setup note: download the PyTorch 2B checkpoint from Kaggle, clone gemma_pytorch, set PYTHONPATH, install dependencies, and run the official script directly.