Qwen 2.5 VL 72B needs ~52.1 GB but RX 7900 XTX 24GB only has 24.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
28.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.6 tok/s
TTFT
75413 ms
Safe context
4K
Memory
52.1 GB / 24.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 52.1 GB, but this setup only exposes 24.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.6 tok/s | 41346 ms | 4K |
| Coding | F | Too heavy | 2.4 tok/s | 82011 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 119289 ms | 4K |
| Reasoning | F | Too heavy | 2.4 tok/s | 96922 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 149111 ms | 4K |
How Qwen 2.5 VL 72B (72B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | F0 |
Q3_K_S | 3 | 35.3 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 177%.
~$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 177%.
~$3,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$8,000 MSRP
NVIDIA H100 80GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$40,000 MSRP
No, Qwen 2.5 VL 72B requires more memory than RX 7900 XTX 24GB provides.
Qwen 2.5 VL 72B (72B parameters) requires approximately 52.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 VL 72B is Q4_K_M, which balances quality and memory efficiency.
On RX 7900 XTX 24GB, Qwen 2.5 VL 72B achieves approximately 2.4 tokens per second decode speed with a time-to-first-token of 82011ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 VL 72B on RX 7900 XTX 24GB receives a F grade with 2.4 tok/s and 4K context.
On RX 7900 XTX 24GB, Qwen 2.5 VL 72B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-2.5-vl-72b-on-rx-7900-xtx-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 43.9 GB | Medium | F0 |
Q5_K_M | 5 | 51.8 GB | High | F0 |
Q6_K | 6 | 59.0 GB | High | F0 |
Q8_0 | 8 | 77.0 GB | Very High | F0 |
F16 | 16 | 147.6 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
