How to run DeepSeek-Coder-V2-Lite Instruct locally

DeepSeek-Coder-V2-Lite Instruct (DeepSeek, 15.7B) runs on your own machine for $0 if you have about 11 GB of VRAM. Here's how to run it with Ollama, LM Studio, or llama.cpp — and what it would cost the other ways.

VRAM to run

~11 GB

Download

~10.4 GB

Quant

Q4_K_M

Context

163.8K

Three ways to run DeepSeek-Coder-V2-Lite Instruct locally

1. Ollama — the one-liner

Auto-downloads a 4-bit quant and starts a chat — the simplest option.

ollama run deepseek-coder-v2:16b

First run downloads ~10.4 GB, then it's offline and free. Get Ollama at ollama.com.

2. LM Studio — point-and-click

Open LM Studio, search “DeepSeek-Coder-V2-Lite Instruct”, and download a quant that fits your VRAM (≈11 GB at Q4_K_M). Load it and chat — fully offline. It also serves a local OpenAI-compatible API you can point Spanvero at.

3. llama.cpp — maximum control

Grab a community GGUF build of DeepSeek-Coder-V2-Lite Instruct from Hugging Face (search “DeepSeek-Coder-V2-Lite Instruct GGUF” — bartowski and unsloth publish reliable ones), then run:

./llama-cli -m <Q4_K_M-file>.gguf -p "Hello" -ngl 99

Or serve it with ./llama-server -m <file>.gguf for an OpenAI-compatible API on :8080.

What it costs — $0 markup

On your machine: $0 — you already have the hardware (needs ~11 GB VRAM).
No GPU big enough? Use your own API key at about $0.23/1M tokens (size estimate), or rent a GPU by the hour. Full cost breakdown →

License: commercial use OK.

Browse: DeepSeek-Coder-V2-Lite Instruct cost · models for your GPU · all models

Open the free Spanvero advisor → — it detects your hardware and confirms what fits.