Can Qwen 3 30B A3B run on NVIDIA L4 24GB?

YES — With Offload

S90Excellent

▲Estimated — low-sample bucket· few comparable runs

Qwen 3 30B A3B needs ~23.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 23.4 GB, 21.2 tok/s, Runs with offload

23.4 GB required24.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

21.2 tok/s

TTFT

9119 ms

Safe context

23K

Memory

23.4 GB / 24.0 GB

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 3 30B A3B on NVIDIA L4 24GB
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: 21.2 tok/s decode · 9.1s TTFT (warm) · 53 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.

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	Tight fit	21.2 tok/s	4974 ms	23K
Coding	S	Runs with offload	21.2 tok/s	9119 ms	23K
Agentic Coding	S	Runs with offload (needs ~0.6 GB host RAM)	14.8 tok/s	19005 ms	23K
Reasoning	S	Runs with offload	21.2 tok/s	10777 ms	23K
RAG	S	Runs with offload (needs ~0.6 GB host RAM)	14.8 tok/s	23757 ms	23K

Quantization options

How Qwen 3 30B A3B (30.5B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	S91
Q3_K_S	3	14.9 GB	Low	S90
NVFP4	4	17.1 GB	Medium	S90
Q4_K_MBest for your GPU	4	18.6 GB	Medium	S90
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

Get started

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

Run

ollama run qwen3:30b-a3b

Your hardware

More models your NVIDIA L4 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.6 35B A3B	35B	A	13.6 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	18.1 tok/s
👁 Alibaba Qwen 3 32B	32B	A	3.3 tok/s

Frequently asked questions

See all results for NVIDIA L4 24GB See all hardware for Qwen 3 30B A3B

URL: https://willitrunai.com/can-run/qwen-3-30b-a3b-on-l4-24gb