Openai Gpt Oss 120B speed on RX 7900 GRE and quantization-level VRAM fit.
RX 7900 GRE does not meet the minimum VRAM requirement for Q4 inference of Openai Gpt Oss 120B. Review the quantization breakdown below to see how higher precision settings impact VRAM and throughput.
RX 7900 GRE lacks sufficient VRAM for comfortable Openai Gpt Oss 120B operation with Q4 quantization.
Your 16GB GPU is 45GB short of the 61GB 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 | 61GB | 16GB | 18.24 tok/s | ❌ Not recommended |
| Q8 | 121GB | 16GB | 12.77 tok/s | ❌ Not recommended |
| FP16 | 241GB | 16GB | 6.93 tok/s | ❌ Not recommended |
Check current pricing links for RX 7900 GRE and similar cards.
Open RX 7900 GRE 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 →Your GPU doesn't meet the VRAM requirements. Run Openai Gpt Oss 120B on cloud GPU instantly.
RX 7900 GRE is not a comfortable Q4 fit for Openai Gpt Oss 120B (about 61GB needed).
Q4 inference is estimated to need about 61GB 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.