Short answer: choose the Radeon RX 7900 XTX when 24GB is enough for your exact model artifact and your tested ROCm stack supports the workload. Choose the GeForce RTX 5090 when the extra 8GB of device memory or the CUDA ecosystem removes a real constraint. The 32GB card creates much safer headroom for common 32B quantizations, but it does not make every 32B file fully resident and it does not prove a speed advantage without a matched benchmark.
The decision in one table
| Planning input | RX 7900 XTX | RTX 5090 Founders Edition |
|---|---|---|
| Architecture | AMD RDNA 3 | NVIDIA Blackwell |
| Memory | 24GB GDDR6 | 32GB GDDR7 |
| Memory interface | 384-bit | 512-bit |
| Published processing units | 6,144 stream processors / 96 compute units | 21,760 CUDA cores |
| Boost clock | Up to 2.5GHz | 2.41GHz |
| Board power | 355W typical board power | 575W total graphics power |
| Recommended system power | 800W | 1,000W |
| Reference power input | 2 × 8-pin | 1 × PCIe Gen 5 cable or included 4 × 8-pin adapter |
| Reference size | 287mm, 2.5-slot | 304mm × 137mm, 2-slot |
| Primary software path | ROCm; verify current OS, framework and operator support | CUDA; verify current Blackwell / compute capability 12.0 support |
Start with the dedicated RX 7900 XTX 24GB guide or the RTX 5090 flagship comparison. For a broader shortlist, compare the best 24GB GPU paths with the best 32GB GPU paths.
What 24GB versus 32GB changes for a 32B model
The table below uses the published file sizes of official Qwen2.5-32B-Instruct GGUF artifacts. “Margin” is simply device memory minus file size. It is not a promise that the whole runtime will fit: KV cache, context length, backend workspaces, display use, and allocator behavior also consume memory.
| Official artifact | File size | 24GB raw margin | 32GB raw margin |
|---|---|---|---|
| Q2_K | 12.3GB | 11.7GB | 19.7GB |
| Q3_K_M | 15.9GB | 8.1GB | 16.1GB |
| Q4_K_M | 19.9GB | 4.1GB | 12.1GB |
| Q5_K_M | 23.3GB | 0.7GB — unsafe planning margin | 8.7GB |
| Q6_K | 26.9GB | Does not fit by file size | 5.1GB |
| Q8_0 | 34.8GB | Does not fit | Does not fit; 2.8GB over |
At Q4_K_M, 32GB increases the raw margin from 4.1GB to 12.1GB. At Q6_K, the file is larger than 24GB but leaves 5.1GB before runtime overhead on 32GB. That is a meaningful capacity boundary—not proof of tokens per second. Use the 32B VRAM guide to plan context and cache, and the matched benchmark form to measure your real workload.
ROCm versus CUDA is part of the hardware decision
AMD’s current ROCm compatibility documentation includes Radeon RX 7000-series support, but the supported combination depends on the ROCm release, operating system, framework, and operator path. NVIDIA’s CUDA ecosystem is broader, yet the RTX 5090’s Blackwell architecture and compute capability 12.0 also require current drivers and software builds. Check the exact stack you intend to run; do not reduce this decision to a brand label. See the CUDA versus ROCm guide.
Power, chassis and connector checks
- Plan around the reference 355W / 800W-system guidance for the RX 7900 XTX and 575W / 1,000W guidance for the RTX 5090. These are not efficiency results.
- Check the exact partner board, not just the reference dimensions. Cable bend clearance and intake space matter.
- The reference Radeon uses two 8-pin connectors. NVIDIA’s Founders Edition uses one Gen 5 cable or the supplied four-8-pin adapter.
- Neither card pools memory across multiple boards for an ordinary single-process workload. Two cards are two separate memory devices unless the runtime explicitly partitions the model.
A buying rule that survives price changes
First choose the smallest artifact, context and cache policy that meets your quality target. If the measured peak comfortably fits inside 24GB and your required operators work on the tested ROCm stack, compare total system price and power. If the workload needs more than 24GB, depends on CUDA-only software, or needs safer 32B headroom, compare the RTX 5090 against other 32GB and higher-memory paths. Then check current offers rather than relying on launch pricing.