Qwen 3 235B A22B needs ~149.5 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
125.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.1 tok/s
TTFT
91748 ms
Safe context
4K
Memory
149.5 GB / 24.0 GB
Offload
80%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 149.5 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 | 2.1 tok/s | 50045 ms | 4K |
| Coding | F | Too heavy | 2.1 tok/s | 91748 ms | 4K |
| Agentic Coding | F | Too heavy | 2.1 tok/s | 133452 ms | 4K |
| Reasoning | F | Too heavy | 2.1 tok/s | 108430 ms | 4K |
| RAG | F | Too heavy | 2.1 tok/s | 166815 ms | 4K |
How Qwen 3 235B A22B (235B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 91.7 GB | Low | F0 |
Q3_K_S | 3 | 115.2 GB | Low | F0 |
NVFP4 | 4 | 131.6 GB | Medium | F0 |
Q4_K_M | 4 | 143.4 GB | Medium | F0 |
Q5_K_M | 5 | 169.2 GB | High | F0 |
Q6_K | 6 | 192.7 GB | High | F0 |
Q8_0 | 8 | 251.5 GB | Very High | F0 |
F16 | 16 | 481.7 GB | Maximum | F0 |
Opções de upgrade
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
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.
~$15,000 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.
~$20,000 MSRP
No, Qwen 3 235B A22B requires more memory than RX 7900 XTX 24GB provides.
Qwen 3 235B A22B (235B parameters) requires approximately 149.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 235B A22B is Q4_K_M, which balances quality and memory efficiency.
On RX 7900 XTX 24GB, Qwen 3 235B A22B achieves approximately 2.1 tokens per second decode speed with a time-to-first-token of 91748ms using Q4_K_M quantization.
For coding workloads, Qwen 3 235B A22B on RX 7900 XTX 24GB receives a F grade with 2.1 tok/s and 4K context.
On RX 7900 XTX 24GB, Qwen 3 235B A22B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-3-235b-a22b-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:
