Qwen3-VL 30B A3B Instruct needs ~23.0 GB but RX 5700 XT 8GB only has 8.0 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
15.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.2 tok/s
TTFT
46615 ms
Safe context
4K
Memory
23.0 GB / 8.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 23.0 GB, but this setup only exposes 8.0 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 | 4.2 tok/s | 25426 ms | 4K |
| Coding | F | Too heavy | 4.2 tok/s | 46615 ms | 4K |
| Agentic Coding | F | Too heavy | 4.2 tok/s | 67803 ms | 4K |
| Reasoning | F | Too heavy | 4.2 tok/s | 55090 ms | 4K |
| RAG | F | Too heavy | 4.2 tok/s | 84754 ms | 4K |
How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | F0 |
Q3_K_S | 3 | 14.7 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
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.
~$1,899 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.
~$2,249 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.
~$2,499 MSRP
No, Qwen3-VL 30B A3B Instruct requires more memory than RX 5700 XT 8GB provides.
Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 23.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3-VL 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.
On RX 5700 XT 8GB, Qwen3-VL 30B A3B Instruct achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 46615ms using Q4_K_M quantization.
For coding workloads, Qwen3-VL 30B A3B Instruct on RX 5700 XT 8GB receives a F grade with 4.2 tok/s and 4K context.
On RX 5700 XT 8GB, Qwen3-VL 30B A3B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, 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-3-vl-30b-a3b-on-rx-5700-xt-8gb" 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 | 18.3 GB | Medium | F0 |
Q5_K_M | 5 | 21.6 GB | High | F0 |
Q6_K | 6 | 24.6 GB | High | F0 |
Q8_0 | 8 | 32.1 GB | Very High | F0 |
F16 | 16 | 61.5 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.
