Deepseek AI Deepseek Coder V2 Lite Instruct speed on RTX 4080 Super and quantization-level VRAM fit.
RTX 4080 Super meets the minimum VRAM requirement for Q4 inference of Deepseek AI Deepseek Coder V2 Lite Instruct. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RTX 4080 Super can run Deepseek AI Deepseek Coder V2 Lite Instruct with Q4 quantization. At approximately 87 tokens/second, you can expect Good speed - acceptable for interactive use.
You have 8GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 8GB | 16GB | 87.10 tok/s | ✅ Fits comfortably |
| Q8 | 16GB | 16GB | 60.97 tok/s | ⚠️ Tight fit |
| FP16 | 32GB | 16GB | 33.10 tok/s | ❌ Not recommended |
Need a GPU with 8GB+ VRAM? These guides match your requirements.
Check current pricing links for RTX 4080 Super and similar cards.
Open RTX 4080 Super buy links →Use workload-focused recommendations before committing to a purchase.
Browse best GPU guides →Compare complete systems if you want ready-to-run hardware.
Compare prebuilt systems →Rent cloud GPUs by the hour — no upfront hardware cost.
RTX 4080 Super can run Deepseek AI Deepseek Coder V2 Lite Instruct at Q4 with an estimated 87 tok/s.
Q4 inference is estimated to need about 8GB VRAM on this page, while RTX 4080 Super has 16GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.