Can Qwen 3 4B run on NVIDIA A2 16GB?

YES — Runs Great

A82Great

○Estimated from fit model

Qwen 3 4B needs ~7.4 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 7.4 GB, 56.0 tok/s, Runs well

7.4 GB required16.0 GB available

46% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

33K

Memory

7.4 GB / 16.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 3 4B on NVIDIA A2 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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	56.0 tok/s	1886 ms	33K
Coding	A	Runs well	56.0 tok/s	3457 ms	33K
Agentic Coding	A	Runs well	56.0 tok/s	5029 ms	33K
Reasoning	A	Runs well	56.0 tok/s	4086 ms	33K
RAG	A	Runs well	56.0 tok/s	6286 ms	33K

Quantization options

How Qwen 3 4B (4B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	A77
Q3_K_S	3	2.0 GB	Low	A78
NVFP4	4	2.2 GB	Medium	A78
Q4_K_M	4	2.4 GB	Medium	A78
Q5_K_M	5	2.9 GB	High	A78
Q6_K	6	3.3 GB	High	A79
Q8_0	8	4.3 GB	Very High	A80
F16Best for your GPU	16	8.2 GB	Maximum	A83

Get started

Copy-paste commands to run Qwen 3 4B on your machine.

Run

ollama run qwen3:4b

Your hardware

More models your NVIDIA A2 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	30.5 tok/s
👁 Alibaba Qwen 3 14B	14B	S	19.7 tok/s
👁 Alibaba Qwen 3 8B	8B	S	34.4 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	18.7 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	17.4 tok/s

Frequently asked questions

See all results for NVIDIA A2 16GB See all hardware for Qwen 3 4B

URL: https://willitrunai.com/can-run/qwen-3-4b-on-a2-16gb