DeepSeekDeepSeek Coder V2 Lite 16B on NVIDIA GeForce RTX 4090
Yes — RTX 4090 runs DeepSeek Coder V2 Lite 16B excellently at Q5_K_M — 55 tok/s. 24 GB VRAM with plenty of headroom.
Model Size
15.7B
Device VRAM
24 GB
Bandwidth
1008 GB/s
Quantization
Q5_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for DeepSeek Coder V2 Lite 16B on NVIDIA GeForce RTX 4090 at Q5_K_M. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q5_K_M | 55 tok/s | 100ms | ✓ Yes | Excellent | estimated |
Notes
Q5_K_M
Q5 at 10.9GB leaves 13GB free on 4090. Lightning fast for a coding model. MoE efficiency stands out.
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 4090
NVIDIA GeForce RTX 4090 has 24 GB at 1008 GB/s. Street price: $1,799.
See all models NVIDIA GeForce RTX 4090 can run →Builds with NVIDIA GeForce RTX 4090
Estimate method: Community benchmarks. Reference hardware source: github.com (2026-01-15)
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.