Can Qwen3-VL 30B A3B Instruct run on RX 7900 XTX 24GB?
YES — With Offload
Qwen3-VL 30B A3B Instruct needs ~23.6 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~108 tok/s.
Operating mode
Choose the run profile you care about
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
Fit status
Runs with offload
Decode
108.1 tok/s
TTFT
1791 ms
Safe context
21K
Memory
23.6 GB / 24.0 GB
Memory breakdown
See how fast it feels
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs with offload | 108.1 tok/s | 977 ms | 21K |
| Coding | S | Runs with offload | 108.1 tok/s | 1791 ms | 21K |
| Agentic Coding | S | Runs with offload (needs ~0.8 GB host RAM) | 74.2 tok/s | 3795 ms | 21K |
| Reasoning | S | Runs with offload | 108.1 tok/s | 2117 ms | 21K |
| RAG | S | Runs with offload (needs ~0.8 GB host RAM) | 74.2 tok/s | 4743 ms | 21K |
Quantization options
How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | S93 |
Q3_K_S | 3 | 14.7 GB | Low | S92 |
NVFP4 | 4 | 16.8 GB | Medium | S92 |
Q4_K_MBest for your GPU | 4 | 18.3 GB | Medium | S92 |
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 |
Get started
Copy-paste commands to run Qwen3-VL 30B A3B Instruct on your machine.
Run
lms load Qwen3-VL-30B-A3B-Instruct && lms server startYour hardware
More models your RX 7900 XTX 24GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | S | 104.5 tok/s |
Frequently asked questions
Can RX 7900 XTX 24GB run Qwen3-VL 30B A3B Instruct?
Yes, RX 7900 XTX 24GB can run Qwen3-VL 30B A3B Instruct with a S grade (Runs with offload). Expected decode speed: 108.1 tok/s.
How much VRAM does Qwen3-VL 30B A3B Instruct need?
Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 23.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen3-VL 30B A3B Instruct?
The recommended quantization for Qwen3-VL 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen3-VL 30B A3B Instruct run at on RX 7900 XTX 24GB?
On RX 7900 XTX 24GB, Qwen3-VL 30B A3B Instruct achieves approximately 108.1 tokens per second decode speed with a time-to-first-token of 1791ms using Q4_K_M quantization.
Can RX 7900 XTX 24GB run Qwen3-VL 30B A3B Instruct for coding?
For coding workloads, Qwen3-VL 30B A3B Instruct on RX 7900 XTX 24GB receives a S grade with 108.1 tok/s and 21K context.
What context window can Qwen3-VL 30B A3B Instruct use on RX 7900 XTX 24GB?
On RX 7900 XTX 24GB, Qwen3-VL 30B A3B Instruct can safely use up to 21K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
What should I upgrade first if Qwen3-VL 30B A3B Instruct feels slow on RX 7900 XTX 24GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
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-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: