Deepseek AI Deepseek Coder V2 Lite Instruct speed on RTX 4070 Ti Super and quantization-level VRAM fit.
RTX 4070 Ti 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.
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Deepseek ai deepseek coder v2 lite instruct speed on rtx 4070 ti super
Deepseek AI Deepseek Coder V2 Lite Instruct runs on RTX 4070 Ti Super at about 170 tok/s at Q4 in our current compatibility dataset.
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RTX 4070 Ti Super can run Deepseek AI Deepseek Coder V2 Lite Instruct with Q4 quantization. At approximately 170 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 15GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 1GB | 16GB | 169.80 tok/s | ✅ Fits comfortably |
| Q8 | 2GB | 16GB | 118.86 tok/s | ✅ Fits comfortably |
| FP16 | 4GB | 16GB | 64.53 tok/s | ✅ Fits comfortably |
Check current pricing links for RTX 4070 Ti Super and similar cards.
Open RTX 4070 Ti 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 →RTX 4070 Ti Super can run Deepseek AI Deepseek Coder V2 Lite Instruct at Q4 with an estimated 170 tok/s.
Q4 inference is estimated to need about 1GB VRAM on this page, while RTX 4070 Ti Super has 16GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.