Qwen 3 30B A3B needs ~16.2 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q2_K quantization, expect ~46 tok/s.
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
6.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
16.8 tok/s
TTFT
11547 ms
Safe context
4K
Memory
22.9 GB / 16.0 GB
Offload
30%
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 18.0 tok/s | 5881 ms | 4K |
| Coding | F | Too heavy | 16.8 tok/s | 11547 ms | 4K |
| Agentic Coding | F | Too heavy | 14.7 tok/s | 19140 ms | 4K |
| Reasoning | F | Too heavy | 16.8 tok/s | 13646 ms | 4K |
| RAG | F | Too heavy | 13.5 tok/s | 26018 ms | 4K |
How Qwen 3 30B A3B (30.5B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.9 GB | Low | F0 |
Q3_K_S | 3 | 14.9 GB | Low | F0 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 3 30B A3B on your machine.
Run
ollama run qwen3:30b-a3bUpgrade options
RTX 4000 Ada 20GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 38%.
~$1,250 MSRP
RTX 3090 24GBBudget pickMakes 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.
~$1,499 MSRP
RTX 4090 24GBNVIDIA upgradeMakes 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.
~$1,599 MSRP
Yes, RTX A4000 16GB can run Qwen 3 30B A3B at Q2_K quantization (Runs with offload (needs ~0.1 GB host RAM)). The recommended Q4_K_M requires 22.9 GB which exceeds available memory, but at Q2_K it needs only 16.2 GB. Expected decode speed: 46.3 tok/s.
Qwen 3 30B A3B (30.5B parameters) requires approximately 22.9 GB at Q4_K_M quantization. On RTX A4000 16GB, it fits at Q2_K using 16.2 GB.
The recommended quantization is Q4_K_M, but on RTX A4000 16GB the best fitting quantization is Q2_K, which uses 16.2 GB.
On RTX A4000 16GB, Qwen 3 30B A3B achieves approximately 46.3 tokens per second decode speed with a time-to-first-token of 4181ms using Q2_K quantization.
For coding workloads, Qwen 3 30B A3B on RTX A4000 16GB receives a F grade with 16.8 tok/s and 4K context.
On RTX A4000 16GB, Qwen 3 30B A3B can safely use up to 14K tokens of context at Q2_K quantization. The model's official context limit is 131K, 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-3-30b-a3b-on-a4000-16gb" 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 | 18.6 GB | Medium | F0 |
Q5_K_M | 5 | 22.0 GB | High | F0 |
Q6_K | 6 | 25.0 GB | High | F0 |
Q8_0 | 8 | 32.6 GB | Very High | F0 |
F16 | 16 | 62.5 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.