v2.2 · 2026-06-12

DeepSeek-Coder-V2 Lite Instruct

deepseek-coder-v2-lite-instruct

CODE

DeepSeek-Coder-V2 Lite Instruct is a 16B code model profile for local AI planning. This page records provider, license, context, quantization, hardware-fit estimates, setup hints, source links, and caveats so readers do not choose a model by name alone.

PROVIDER

DeepSeek

FAMILY

DeepSeek-Coder-V2

MODEL TYPE

code

PARAMETERS

16B (2.4B active)

MODALITIES

text, code

ARCHITECTURE

Mixture-of-Experts code model based on DeepSeekMoE

CONTEXT WINDOW

128,000 tokens

TRAINING TOKENS

—

RELEASE DATE

2024-06

LICENSE & LINKS

DeepSeek license / MIT repository materialslicense text

MODEL CARD

huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct

HUGGING FACE

deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct

GGUF REPOSITORIES

bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF community conversion

Community GGUF repository referenced by the June 2026 model research.

SETUP HINTS

OLLAMA

ollama run deepseek-coder-v2:lite

LM STUDIO

Available according to the supplied June 2026 research; select a cited GGUF/MLX build and verify current app metadata before relying on hands-on setup guidance.

LLAMA.CPP

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

QUANTIZATION FILE SIZES (GGUF)

Q4_K_M

10.36 GB

Q5_K_M

11.85 GB

Q8_0

16.7 GB

Quantization note: Q4_K_M for first local attempts unless a cited runtime recommends a different default.

RAM / VRAM ESTIMATES

MIN RAM

16 GB

COMFORTABLE RAM

32 GB

MIN VRAM

16 GB

COMFORTABLE VRAM

32 GB

These are conservative local-inference estimates, not official hardware requirements.

HARDWARE FIT

CPU only (no GPU)

limited

8 GB RAM

16 GB RAM

limited

32 GB RAM

comfortable

8 GB VRAM

12 GB VRAM

24 GB VRAM

limited

Apple Silicon (unified memory)

limited

Hardware fit values are conservative local-inference estimates based on GGUF size plus runtime and KV-cache overhead. Actual requirements depend on context length, quantization, runtime, GPU offload, and other running apps.

BEST FOR

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

AVOID IF

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

CAVEATS

·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.

·Commercial-use status from the supplied research: Likely usable commercially, but review DeepSeek-Coder-V2 model license before redistribution.

·Beginner summary from supplied research: 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.

SOURCE URLS