Kaitchup Phi 3 Mini 4K Instruct Gptq 4bit speed on RTX 4090 and quantization-level VRAM fit.
RTX 4090 meets the minimum VRAM requirement for Q4 inference of Kaitchup Phi 3 Mini 4K Instruct Gptq 4bit. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RTX 4090 can run Kaitchup Phi 3 Mini 4K Instruct Gptq 4bit with Q4 quantization. At approximately 180 tokens/second, you can expect Excellent speed - conversational response times under 1 second.
You have 22GB headroom, which is sufficient for system overhead and smooth operation.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 2GB | 24GB | 180.00 tok/s | ✅ Fits comfortably |
| Q8 | 4GB | 24GB | 126.00 tok/s | ✅ Fits comfortably |
| FP16 | 8GB | 24GB | 68.40 tok/s | ✅ Fits comfortably |
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Compare prebuilt systems →RTX 4090 can run Kaitchup Phi 3 Mini 4K Instruct Gptq 4bit at Q4 with an estimated 180 tok/s.
Q4 inference is estimated to need about 2GB VRAM on this page, while RTX 4090 has 24GB available.
If you need more speed or context headroom, compare alternative GPUs below and check higher-tier VRAM options.