DeepSeek Coder V2 Lite (16B)
MoE coding specialist — 16B total / 2.4B active. Fast on 12GB cards.
Our verdict
Positioning
The right coder when 8–12 GB VRAM is the constraint.
Strengths
- 2.4B active per token — autocomplete-fast even on mid-tier GPUs.
- DeepSeek license — clean for commercial use.
- Strong fill-in-the-middle comparable to Codestral 22B at lower VRAM.
Limitations
- MoE total memory still ~10 GB at Q4 — not as VRAM-frugal as the active-parameter count suggests.
- Qwen 2.5 Coder 32B is stronger when VRAM allows.
- Long-context behavior less polished than dedicated long-context coders.
Performance evidence
The earlier timing ranges were not tied to a published run. Use the benchmark records for measured results and compare their full configurations.
Should you run this locally?
Yes, for 12–16 GB VRAM coders who want autocomplete speed without sacrificing quality. No, for 24 GB+ owners — Qwen 2.5 Coder 32B is materially stronger.
How it compares
- vs Qwen 2.5 Coder 32B → Qwen is stronger; DeepSeek Coder V2 Lite is faster and lower VRAM.
- vs Codestral 22B → Codestral has higher ceiling; DeepSeek Coder V2 Lite has MoE speed advantage.
- vs CodeGemma 7B → DeepSeek Coder V2 Lite wins on quality; CodeGemma wins on absolute VRAM minimum.
Run this yourself
ollama pull deepseek-coder-v2:lite-instruct-q4_K_M
ollama run deepseek-coder-v2:lite-instruct-q4_K_M
›Why this rating
8.0/10 — DeepSeek's compact coder MoE (16B total, 2.4B active). Genuinely fast for what it produces, and license-clean. Loses points to Qwen 2.5 Coder 32B which is materially stronger if VRAM allows.
Overview
MoE coding specialist — 16B total / 2.4B active. Fast on 12GB cards.
Featured in this stack
The L3 execution stacks that pick this model as a recommended component, with the one-line note explaining the role it plays in each.
- Stack · L3·Workstation tier·Role: Coding model with strong reasoningBuild a memory-enabled local agent stack (May 2026)
DeepSeek Coder V2 Lite over Qwen 2.5 Coder for memory-heavy workflows: stronger at synthesizing across retrieved memory chunks (real test: better at 'reconcile session 3's plan with session 5's findings'). Qwen 2.5 Coder wins on raw HumanEval; DeepSeek V2 Lite wins on multi-turn synthesis.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- Fast MoE coder
- 338 languages
Weaknesses
- Outpaced by Qwen 2.5 Coder 32B
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 9.5 GB | 12 GB |
Get the model
Ollama
One-line install
ollama run deepseek-coder-v2:16bRead our Ollama review →HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of DeepSeek Coder V2 Lite (16B).
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run DeepSeek Coder V2 Lite (16B)?
Can I use DeepSeek Coder V2 Lite (16B) commercially?
What's the context length of DeepSeek Coder V2 Lite (16B)?
How do I install DeepSeek Coder V2 Lite (16B) with Ollama?
Source: huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify DeepSeek Coder V2 Lite (16B) runs on your specific hardware before committing money.