DeepSeekDeepSeek Coder V2 Lite 16B on NVIDIA RTX 4080 Laptop (120-150W)
Yes — RTX 4080 Laptop (120-150W) handles DeepSeek Coder V2 Lite 16B well at Q4_K_M — 39 tok/s. Solid daily-driver performance on 12 GB VRAM.
Model Size
15.7B
Device VRAM
12 GB
Bandwidth
384 GB/s
Quantization
Q4_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for DeepSeek Coder V2 Lite 16B on NVIDIA RTX 4080 Laptop (120-150W) at Q4_K_M. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q4_K_M | 39 tok/s | 200ms | ✓ Yes | Good | estimated |
Notes
Q4_K_M
Sustained post-thermal-throttle performance. TDP-capped laptop variant.
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 RTX 4080 Laptop (120-150W)
NVIDIA RTX 4080 Laptop (120-150W) has 12 GB at 384 GB/s. Street price: $0.
See all models NVIDIA RTX 4080 Laptop (120-150W) can run →Estimate method: Community laptop benchmarks and thermal throttle reports. Reference hardware source: reddit.com (2026-03-14)
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.