01What it can do
- Apache 2.0
- MMLU 79.7
- Good VRAM compromise
- —Native 32k context (131k via YaRN)
05Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for Qwen 2.5 14B Instruct?
To run Qwen 2.5 14B Instruct locally with Q4 quantization, you need about 9 GB of VRAM. An option to compare: RTX 5070 12GB (ASUS Prime OC) — leave some headroom for the system and context; check engine compatibility with the GPU.
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On the go: Qwen 2.5 14B Instruct also runs on a RTX laptop PC (16 GB of VRAM) →
Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
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- PDF + files
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03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.
04Public benchmarks
Scores reproduced from model cards or MMLU-Pro / community sources. Unit: % correct answers.