Short answer: choose the Radeon RX 7900 XTX when the workload needs more than 16GB and your verified ROCm stack works. Choose the GeForce RTX 4070 Ti SUPER when 16GB is enough and CUDA compatibility, lower reference board power, or a potentially lower complete-system cost matters more. This is a capacity-and-software decision before it is a speed comparison.
RX 7900 XTX versus RTX 4070 Ti SUPER specifications
| Planning input | RX 7900 XTX reference | RTX 4070 Ti SUPER reference |
|---|---|---|
| Architecture | AMD RDNA 3 | NVIDIA Ada Lovelace |
| Memory | 24GB GDDR6 | 16GB GDDR6X |
| Memory interface | 384-bit | 256-bit |
| Published processing units | 6,144 stream processors / 96 compute units | 8,448 CUDA cores |
| Boost clock | Up to 2.5GHz | 2.61GHz |
| Board power | 355W typical board power | 285W total graphics power |
| Recommended system power | 800W | 700W |
| Reference power input | 2 × 8-pin | 1 × 300W+ Gen 5 cable or supplied 2 × 8-pin adapter |
| Reference dimensions | 287mm, 2.5-slot | Varies by manufacturer |
| Primary software path | ROCm; verify OS, framework and operators | CUDA, compute capability 8.9 |
Read the RX 7900 XTX guide, RTX 4070 Ti SUPER guide, 24GB buyer hub and 16GB buyer hub.
The 32B model boundary
| Official Qwen2.5-32B artifact | File size | 16GB raw margin | 24GB raw margin |
|---|---|---|---|
| Q2_K | 12.3GB | 3.7GB | 11.7GB |
| Q3_K_M | 15.9GB | 0.1GB — unsafe | 8.1GB |
| Q4_K_M | 19.9GB | Does not fit by file size | 4.1GB |
| Q5_K_M | 23.3GB | Does not fit | 0.7GB — unsafe |
| Q6_K | 26.9GB | Does not fit | Does not fit; 2.9GB over |
The official Q4_K_M file is 3.9GB larger than a 16GB pool before overhead but leaves 4.1GB of raw margin on 24GB. The Q3_K_M file leaves only 0.1GB on 16GB, which is not a safe planning margin. Use the 32B requirements guide and benchmark builder for the exact context and runtime.
ROCm versus CUDA and matched testing
The Radeon path depends on a supported ROCm release, OS, framework and operator set. The RTX 4070 Ti SUPER can be the more usable option for CUDA-dependent applications even with less memory. Use the CUDA versus ROCm checklist, then hold artifact, quantization, context, cache, batch, concurrency and timing constant. Record driver/runtime, unsupported operators, fallback, CPU offload, exposed memory, peak memory, board power limit and wall power. Never normalize an offloaded 16GB run against a resident 24GB run.
After both paths pass the fit and software checks, compare current screened offers, exact partner boards and seller condition. Do not assume multiple cards create one transparent memory pool.