Can Qwen 3 30B A3B run on NVIDIA T4 16GB?

YES — With Q2_K

S91Excellent

○Estimated from fit model

Qwen 3 30B A3B needs ~16.2 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q2_K quantization, expect ~30 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Balanced

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.

Qwen 3 30B A3B at Q4_K_M needs 22.9 GB — too much for NVIDIA T4 16GB (16.0 GB). Runs at Q2_K (16.2 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.9 GB, exceeds 16.0 GB available

22.9 GB required16.0 GB available

143% VRAM needed

6.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.5 tok/s

TTFT

18492 ms

Safe context

Memory

22.9 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3 30B A3B on NVIDIA T4 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 10.5 tok/s decode · 18.5s TTFT (warm) · 26 tok/s prefill

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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	F	Too heavy	10.3 tok/s	10212 ms	4K
Coding	F	Too heavy	9.6 tok/s	20110 ms	4K
Agentic Coding	F	Too heavy	8.4 tok/s	33523 ms	4K
Reasoning	F	Too heavy	9.6 tok/s	23766 ms	4K
RAG	F	Too heavy	8.4 tok/s	41904 ms	4K

Quantization options

How Qwen 3 30B A3B (30.5B params) fits at each quantization level on NVIDIA T4 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

Get started

Copy-paste commands to run Qwen 3 30B A3B on your machine.

Run

ollama run qwen3:30b-a3b

Upgrade options

Hardware that runs Qwen 3 30B A3B well

👁 NVIDIA
RTX 4000 Ada 20GBBest value

20 GB VRAM (+4)360 GB/s (+40)

Makes the model fit on the accelerator instead of staying completely out of reach.23.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 121%.

~$1,250 MSRP

👁 NVIDIA
RTX 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+616)

Makes the model fit on the accelerator instead of staying completely out of reach.99.1 tok/s decode

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.

~$1,499 MSRP

👁 NVIDIA
RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.85.4 tok/s decode

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.

~$1,599 MSRP

Frequently asked questions

See all results for NVIDIA T4 16GB See all hardware for Qwen 3 30B A3B

URL: https://willitrunai.com/can-run/qwen-3-30b-a3b-on-t4-16gb

⇱

Can Qwen 3 30B A3B run on NVIDIA T4 16GB?

YES — With Q2_K

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Qwen 3 30B A3B well

Frequently asked questions