DeepSeekDeepSeek Coder V2 Lite 16B on NVIDIA GeForce RTX 4060 8GB
Yes — RTX 4060 8GB handles DeepSeek Coder V2 Lite 16B well at Q3_K_M — 45 tok/s. Solid daily-driver performance on 8 GB VRAM.
Model Size
15.7B
Device VRAM
8 GB
Bandwidth
272 GB/s
Quantization
Q3_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for DeepSeek Coder V2 Lite 16B on NVIDIA GeForce RTX 4060 8GB at Q3_K_M. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q3_K_M | 45 tok/s | 180ms | ✓ Yes | Good | estimated |
Notes
Q3_K_M
MoE architecture makes this 15.7B total model run like a small model. Fast coding performance for 8GB.
About DeepSeek Coder V2 Lite 16B
DeepSeek Coder V2 Lite 16B (15.7B) is a coding, ai coding, ai building model. 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.
View all DeepSeek Coder V2 Lite 16B hardware options →About NVIDIA GeForce RTX 4060 8GB
NVIDIA GeForce RTX 4060 8GB has 8 GB at 272 GB/s. Street price: $289.
See all models NVIDIA GeForce RTX 4060 8GB can run →Estimate method: MoE model: only ~2.4B active per token. Q3_K_M 7.4GB fits in 8GB. Reference hardware source: github.com (2026-03-15)
Performance varies by driver version, inference engine, quantization method, context length, and system configuration. Figures shown are estimates based on community benchmarks and may not reflect your exact setup. Product names are trademarks of their respective owners. OwnRig is independent and not affiliated with any hardware or AI model provider.