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Local LLM Guide

Model

DeepSeek-Coder-V2 Lite Instruct

Start with a supported compressed build and short context. 32GB RAM estimate. Best use: Local coding, repo-aware prompts, math-heavy programming, and privacy-sensitive development on midrange hardware for learners too.

16B total / 2.4B activeCode

Best use

Local coding, repo-aware prompts, math-heavy programming, and privacy-sensitive development on midrange hardware for learners too.

Memory planning

32GB RAM estimate

Expected pace

Depends on your hardware, app, and conversation length

License

DeepSeek Model License; read the model’s terms before use

Who should use this model?

  • Local coding, repo-aware prompts, math-heavy programming, and privacy-sensitive development on midrange hardware for learners too.

Choose something else when

  • You need general chat, polished writing, or maximum reasoning outside programming tasks and noncoding workflows instead.

What hardware makes sense?

The download is only part of the memory needed. Leave room for the app, the input it processes, and other open programs. For chat models, context includes the instructions, conversation, and answer. Longer context needs more memory.

CPU only
Memory or response time may limit use. Try a smaller model first.
8GB system memory
Use a smaller model for this memory limit.
16GB system memory
Memory or response time may limit use. Try a smaller model first.
32GB system memory
A possible starting point with a compressed model and short conversation; check the exact file size.
8GB GPU memory
Use a smaller model for this memory limit.
12GB GPU memory
Use a smaller model for this memory limit.
24GB GPU memory
Memory or response time may limit use. Try a smaller model first.
Apple unified memory
Memory or response time may limit use. Try a smaller model first.

Model facts that change the decision

Parameters
16B total / 2.4B active
Input
Text · Code
Published context limit
128,000 tokens
Model format
safetensors · GGUF · Ollama · MLX
Suggested compression
Q4_K_M for first local attempts unless a cited runtime recommends a different default.
Typical Q4 file
10.36GB before runtime and context overhead
Languages
Unknown

First setup

Ollama

ollama run deepseek-coder-v2:lite

LM Studio: Check that your installed app supports the exact model format and task before downloading it.

llama.cpp

Use the cited GGUF repository (bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF) with current llama.cpp-compatible tooling.

Conditions that change the answer

  • MoE does not mean the whole model fits like a 2.4B dense model; the file still stores about 16B parameters.
  • 128K context can be memory-intensive.
  • Best positioned as a coding model, not a general assistant.
  • The model weights use the DeepSeek Model License, with use restrictions and redistribution conditions. The repository code's MIT license does not replace the model license.
  • DeepSeek-Coder-V2 Lite Instruct is the best DeepSeek coding model to keep for local users. Its MoE design activates only 2.4B parameters, while supporting 128K context and stronger code/math behavior than Coder 6.7B for developers locally.

Read the controlling license text →