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

YES — Runs Great

A71Great

Estimated from fit model

Gemma 3 4B needs ~7.3 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) — 7.3 GB, 56.0 tok/s, Runs well

7.3 GB required16.0 GB available

46% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

83K

Memory

7.3 GB / 16.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.1 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 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	B	Runs well	56.0 tok/s	1886 ms	83K
Coding	A	Runs well	56.0 tok/s	3457 ms	83K
Agentic Coding	A	Runs well	56.0 tok/s	5029 ms	83K
Reasoning	A	Runs well	56.0 tok/s	4086 ms	83K
RAG	A	Runs well	56.0 tok/s	6286 ms	83K

Quantization options

How Gemma 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	B67
Q3_K_S	3	2.0 GB	Low	B67
NVFP4	4	2.2 GB	Medium	B68
Q4_K_M	4	2.4 GB	Medium	B68
Q5_K_M	5	2.9 GB	High	B68
Q6_K	6	3.3 GB	High	B68
Q8_0	8	4.3 GB	Very High	B69
F16Best for your GPU	16	8.2 GB	Maximum	A73

Get started

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

Run

ollama run gemma3:4b

Your hardware

More models your NVIDIA A2 16GB can run

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

Frequently asked questions

Can NVIDIA A2 16GB run Gemma 3 4B?

Yes, NVIDIA A2 16GB can run Gemma 3 4B with a A grade (Runs well). Expected decode speed: 56.0 tok/s.

How much VRAM does Gemma 3 4B need?

Gemma 3 4B (4B parameters) requires approximately 7.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 4B?

The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 4B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Gemma 3 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Gemma 3 4B for coding?

For coding workloads, Gemma 3 4B on NVIDIA A2 16GB receives a A grade with 56.0 tok/s and 83K context.

What context window can Gemma 3 4B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Gemma 3 4B can safely use up to 83K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

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

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

56.0 tok/s

1886 ms

83K

Coding

Runs well

56.0 tok/s

3457 ms

83K

Agentic Coding

Runs well

56.0 tok/s

5029 ms

83K

Reasoning

Runs well

56.0 tok/s

4086 ms

83K

RAG

Runs well

56.0 tok/s

6286 ms

83K

Quant

Bits

VRAM

Quality

Fit

Q2_K

1.6 GB

Low

B67

Q3_K_S

2.0 GB

Low

B67

NVFP4

2.2 GB

Medium

B68

Q4_K_M

2.4 GB

Medium

B68

Q5_K_M

2.9 GB

High

B68

Q6_K

3.3 GB

High

B68

Q8_0

4.3 GB

Very High

B69

F16Best for your GPU

8.2 GB

Maximum

A73

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

30.5 tok/s

Qwen 3 14B

14B

19.7 tok/s

Qwen 3 8B

34.4 tok/s

Phi-4-reasoning-plus 14B

14.7B

18.7 tok/s

GPT-OSS 20B

21B

17.4 tok/s

Frequently asked questions

Can NVIDIA A2 16GB run Gemma 3 4B?

Yes, NVIDIA A2 16GB can run Gemma 3 4B with a A grade (Runs well). Expected decode speed: 56.0 tok/s.

How much VRAM does Gemma 3 4B need?

Gemma 3 4B (4B parameters) requires approximately 7.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 4B?

The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 4B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Gemma 3 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Gemma 3 4B for coding?

For coding workloads, Gemma 3 4B on NVIDIA A2 16GB receives a A grade with 56.0 tok/s and 83K context.

What context window can Gemma 3 4B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Gemma 3 4B can safely use up to 83K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.