8 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 continuously updated local open-model installation note. Starting from a Windows + Ubuntu 26.04 LTS development machine with 64GB host memory, 32GB assigned to WSL, and an RTX 4060 Laptop GPU with 8GB VRAM, this article first decides whether Ollama should run on Windows or WSL, compares llama.cpp, LM Studio, vLLM, and other options, and then lists models worth keeping locally.
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 practical record of turning idle RTX 4090 machines into local model servers with NVIDIA drivers, Ollama, OpenAI-compatible APIs, model downloads, benchmark scripts, and first-round model evaluation.
A practical Windows 11 setup note for running DeepSeek R1 locally through Ollama, then connecting it to Chatbox, MaxKB, Open WebUI, and VS Code-based AI tools.