01What it can do
- 128k code context
- Efficient MoE
- Very good at code generation and correction
- DeepSeek license
- —Lite version less powerful than 236B
- —Worse than Qwen 2.5 Coder 32B
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 DeepSeek Coder V2 Lite 16B?
To run DeepSeek Coder V2 Lite 16B locally with Q4 quantization, you need about 10 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: DeepSeek Coder V2 Lite 16B also runs on a RTX laptop PC (16 GB of VRAM) →
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.