DeepSeek Coder V2 Lite 16B
DeepSeek · DeepSeek License
Mixture-of-Experts architecture: 15.7B total, ~2.4B active per token. Fast and capable code generation and completion. Inference speed belies the total param count. One of the best coding models for its effective size.
- Parameters
- 15.7B
- Architecture
- Dense
- Context
- 163,840 tokens
- Released
- 2024-06-17
- Engines
- llama.cpp, ollama, vLLM
- Builder Tools
- Cursor, Continue, Aider, Windsurf
Parameters
15.7B
VRAM
10.9 GB
Context
160K
Formats
4
GPUs
23
DeepSeek Coder V2 Lite 16B (15.7B) requires 10.9 GB VRAM at recommended quality (Q5_K_M). At efficient quality (Q4_K_M), it fits in 9.1 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 210 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.
Source: OwnRig methodology
10.9 GB
Q5_K_M
9.4 GB
160K tokens
Coding
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 16.9 GB | 15.7 GB |
| recommended | Q5_K_M | 10.9 GB | 9.4 GB |
| efficient | Q4_K_M | 9.1 GB | 7.9 GB |
| compressed | Q3_K_M | 7.4 GB | 6.1 GB |
Context Length Impact
KV cache VRAM at Q5_K_M quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 205 MB | 11.1 GB |
| 4K | 410 MB | 11.3 GB |
| 8K | 819 MB | 11.7 GB |
| 16K | 1.5 GB | 12.4 GB |
| 32K | 3.1 GB | 14 GB |
| 64K | 6.1 GB | 17 GB |
| 128K | 12.3 GB | 23.2 GB |
Compatible GPUs
23 devicesShowing 23 of 23 entries
Builder Context
DeepSeek Coder V2 Lite 16B is commonly used with Cursor, Continue, Aider, Windsurf. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
Recommended Builds
Complete PC builds that can run DeepSeek Coder V2 Lite 16B.
Frequently Asked Questions
- How much VRAM does DeepSeek Coder V2 Lite 16B need?
- DeepSeek Coder V2 Lite 16B requires 10.9 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 7.4 GB.
- What is the best GPU for DeepSeek Coder V2 Lite 16B?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for DeepSeek Coder V2 Lite 16B, achieving 210 tok/s at Q8_0 with an excellent rating.
- Can I run DeepSeek Coder V2 Lite 16B on an RTX 4060 Ti?
- Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, DeepSeek Coder V2 Lite 16B runs at 50 tok/s (Q5_K_M, excellent).
- What quantization should I use for DeepSeek Coder V2 Lite 16B?
- For the best quality, use Q5_K_M (10.9 GB VRAM). If your GPU has limited VRAM, Q3_K_M (7.4 GB) is the most efficient option with acceptable quality.
- Is DeepSeek Coder V2 Lite 16B good for coding?
- Yes. DeepSeek Coder V2 Lite 16B is used with Cursor, Continue, Aider, Windsurf for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.
Data confidence: verified. Source
VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.DeepSeek is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.