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.

In practice, DeepSeek Coder V2 Lite 16B needs a 12 GB card at Q4_K_M (10 GB). Stepping up to Q8_0 nearly doubles the footprint to 18 GB, and unquantized FP16 weights take 32 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, DeepSeek Coder V2 Lite 16B needs roughly 18 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 5 tokens/sec on entry-level GPUs, on the order of 18 tokens/sec on a mid-range card, and up to 45 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches DeepSeek Coder V2 Lite 16B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

GPU memory	Example cards	Best fit for DeepSeek Coder V2 Lite 16B
8 GB	RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB	Does not fit — needs 10 GB at Q4_K_M
12 GB	RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop	Q5_K_M (12 GB used)
16 GB	RTX 5080, RTX 4080 Super, Radeon RX 9070 XT	Q5_K_M (12 GB used)
24 GB	RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop	Q8_0 (18 GB used)
32 GB	RTX 5090	FP16 (32 GB used)

Which GPU should you buy to run DeepSeek Coder V2 Lite 16B?

To run DeepSeek Coder V2 Lite 16B locally at Q4, you need ~10 GB of VRAM. The best value for this is a RTX 5070 (12 GB VRAM).

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.

To put DeepSeek Coder V2 Lite 16B in context: its HumanEval score of 81.1 ranks #14 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.

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

Typical workloads

In our catalog grid, DeepSeek Coder V2 Lite 16B is filed under Code, Refactor — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline).

The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.

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.

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Frequently asked questions

How much VRAM does DeepSeek Coder V2 Lite 16B need?

At the recommended Q4_K_M quantization, DeepSeek Coder V2 Lite 16B needs about 10 GB of VRAM. Q8_0 takes 18 GB, and unquantized FP16 weights take 32 GB.

Can DeepSeek Coder V2 Lite 16B run without a GPU?

Yes — with roughly 18 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does DeepSeek Coder V2 Lite 16B support?

DeepSeek Coder V2 Lite 16B supports a 128k-token context window (131,072 tokens).

Can I use DeepSeek Coder V2 Lite 16B commercially?

Yes. DeepSeek Coder V2 Lite 16B is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is DeepSeek Coder V2 Lite 16B on consumer hardware?

Our compatibility engine estimates on the order of 18 tokens/sec on a mid-range GPU and up to 45 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of DeepSeek Coder V2 Lite 16B should I download first?

Start with Q4_K_M (10 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q8_0.