DeepSeek Coder V2 Lite 16B

Overview

A 16B MoE code specialist from DeepSeek covering 338 programming languages with a 128k context. Fast inference for its quality tier.

When to pick this model

• Code generation across uncommon or niche languages
• Repo-scale code Q&A using the 128k window
• Local code assistants where MoE inference speed matters
• Bug fixing and refactoring tasks
• MIT-licensed code tooling

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	10 GB
Q5_K_M	12 GB
Q8_0	18 GB
FP16 (no quantization)	32 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Benchmark	Score
HumanEval	81.1
LiveCodeBench	28.8

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• 128k context for code
• MoE architecture keeps inference fast
• Coverage of 338 programming languages
• Strong code generation and repair

Limitations

• Lite version trails the 236B DeepSeek Coder V2 by a wide margin
• Beaten by Qwen 2.5 Coder 32B on standard benchmarks
• MoE memory footprint is larger than active params suggest

Architecture & training

Architecture: Lightweight MoE · DeepSeek Coder V2 Lite · 16B · 128k context

Training: DeepSeek V2 Lite code pre-training + fine-tuning on 338 languages.

Quick start

ollama run deepseek-coder-v2:16b-lite-instruct

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.