Qwen 3 30B A3B needs ~23.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~24 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
3.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2.4 GB host RAM)
Decode
23.8 tok/s
TTFT
8136 ms
Safe context
4K
Memory
23.0 GB / 20.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Very compromised (needs ~1.9 GB host RAM) | 25.5 tok/s | 4145 ms | 4K |
| Coding | A | Very compromised (needs ~2.4 GB host RAM) | 23.8 tok/s | 8136 ms | 4K |
| Agentic Coding | F | Too heavy | 20.9 tok/s | 13480 ms | 4K |
| Reasoning | A | Very compromised (needs ~2.4 GB host RAM) | 23.8 tok/s | 9616 ms | 4K |
| RAG | F | Too heavy | 20.9 tok/s | 16849 ms |
How Qwen 3 30B A3B (30.5B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.9 GB | Low | S91 |
Q3_K_SBest for your GPU | 3 | 14.9 GB | Low | S90 |
Copy-paste commands to run Qwen 3 30B A3B on your machine.
Run
ollama run qwen3:30b-a3bYes, RTX 4000 Ada 20GB can run Qwen 3 30B A3B with a A grade (Very compromised (needs ~2.4 GB host RAM)). Expected decode speed: 23.8 tok/s.
Qwen 3 30B A3B (30.5B parameters) requires approximately 23.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 30B A3B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4000 Ada 20GB, Qwen 3 30B A3B achieves approximately 23.8 tokens per second decode speed with a time-to-first-token of 8136ms using Q4_K_M quantization.
For coding workloads, Qwen 3 30B A3B on RTX 4000 Ada 20GB receives a A grade with 23.8 tok/s and 4K context.
On RTX 4000 Ada 20GB, Qwen 3 30B A3B can safely use up to 4K tokens of context. 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-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
17.1 GB |
| 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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.