AMD RX 7900 GRE - 16GB usable
Specifications
| Brand | AMD |
|---|---|
| Model | RX 7900 GRE |
| Usable VRAM | 16GB |
| Architecture | RDNA3 |
| CUDA / Stream Processors | 5,120 |
| Memory Bandwidth | 576 GB/s |
| TDP | 260W |
| FP32 TFLOPS | 46 |
Current Offers
Used from $535New from $900
Prices last updated:
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Price History
- eBay$535mid-range
- Amazon$800current
- Newegg$900mid-range
For AI / LLM Use
Good for 14B models. 30B requires aggressive quantization. Verify ROCm support for the exact framework and operating system you plan to use.
What Models Can It Run?
- 14B Q6_K, 30B Q3_K (tight)
- 14B Q4_K_M, 7B full precision
- 7B Q6_K, 14B Q3_K (tight)
- 7B Q4_K_M only
Estimated Performance
Generation: ~43 tokens/sec
Prefill: ~821 tokens/sec
Recommended Quantisations
- Q4_K_M for 14B models
- Q6_K for 7B-8B models
- Q8 for 7B if VRAM allows
Pros & Cons
Pros
- RDNA3 architecture: supported by current ROCm stacks
- Consumer card: easy to install, display output
Cons
- 16GB usable VRAM: may need quantization for 30B+ models
- Moderate memory bandwidth: not the fastest for inference
- ROCm and CUDA-first framework compatibility should be checked before purchase
Community Verdict
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