Qwen3-VL 30B A3B Instruct needs ~23.6 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~31 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
Fit status
Runs with offload
Decode
30.5 tok/s
TTFT
6349 ms
Safe context
21K
Memory
23.6 GB / 24.0 GB
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 | S | Runs with offload | 30.5 tok/s | 3463 ms | 21K |
| Coding | S | Runs with offload | 30.5 tok/s | 6349 ms | 21K |
| Agentic Coding | S | Runs with offload (needs ~0.8 GB host RAM) | 20.9 tok/s | 13451 ms | 21K |
| Reasoning | S | Runs with offload | 30.5 tok/s | 7503 ms | 21K |
| RAG | S | Runs with offload (needs ~0.8 GB host RAM) | 20.9 tok/s | 16813 ms |
How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on NVIDIA L4 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 |
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
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | S | 29.5 tok/s |
Yes, NVIDIA L4 24GB can run Qwen3-VL 30B A3B Instruct with a S grade (Runs with offload). Expected decode speed: 30.5 tok/s.
Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 23.6 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 NVIDIA L4 24GB, Qwen3-VL 30B A3B Instruct achieves approximately 30.5 tokens per second decode speed with a time-to-first-token of 6349ms using Q4_K_M quantization.
For coding workloads, Qwen3-VL 30B A3B Instruct on NVIDIA L4 24GB receives a S grade with 30.5 tok/s and 21K context.
On NVIDIA L4 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-3-vl-30b-a3b-on-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 21K |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.