Dphn Dolphin 2.9.1 Yi 1.5 34B speed on RX 7900 GRE and quantization-level VRAM fit.
RX 7900 GRE does not meet the minimum VRAM requirement for Q4 inference of Dphn Dolphin 2.9.1 Yi 1.5 34B. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RX 7900 GRE lacks sufficient VRAM for comfortable Dphn Dolphin 2.9.1 Yi 1.5 34B operation with Q4 quantization.
Your 16GB GPU is 2GB short of the 18GB minimum.
Options: (1) Try Q2 or Q3 quantization for lower VRAM requirements, (2) Consider cloud GPU rental, (3) Upgrade to a GPU with at least 16GB VRAM.
| Quantization | VRAM needed | VRAM available | Estimated speed | Verdict |
|---|---|---|---|---|
| Q4 | 18GB | 16GB | 31.92 tok/s | ❌ Not recommended |
| Q8 | 35GB | 16GB | 22.35 tok/s | ❌ Not recommended |
| FP16 | 69GB | 16GB | 12.13 tok/s | ❌ Not recommended |
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Compare prebuilt systems →Your GPU doesn't meet the VRAM requirements. Run Dphn Dolphin 2.9.1 Yi 1.5 34B on cloud GPU instantly.
RX 7900 GRE is not a comfortable Q4 fit for Dphn Dolphin 2.9.1 Yi 1.5 34B (about 18GB needed).
Q4 inference is estimated to need about 18GB VRAM on this page, while RX 7900 GRE has 16GB available.
Try lower-bit quantization, choose a smaller model, or move to a higher-VRAM GPU from the alternatives list.