Can Gemma 3 12B run on NVIDIA A2 16GB?

YES — Tight Fit

A79Great

Estimated from fit model

Gemma 3 12B needs ~15.0 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 15.0 GB, 22.4 tok/s, Tight fit

15.0 GB required16.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

22.4 tok/s

TTFT

8652 ms

Safe context

19K

Memory

15.0 GB / 16.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 3 12B on NVIDIA A2 16GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 22.4 tok/s decode · 8.7s TTFT (warm) · 56 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	A	Runs well	22.4 tok/s	4719 ms	19K
Coding	A	Tight fit	22.4 tok/s	8652 ms	19K
Agentic Coding	F	Too heavy	10.6 tok/s	26517 ms	19K
Reasoning	A	Tight fit	22.4 tok/s	10225 ms	19K
RAG	F	Too heavy	10.6 tok/s	33146 ms	19K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A78
Q3_K_S	3	5.9 GB	Low	A79
NVFP4	4	6.7 GB	Medium	A80
Q4_K_M	4	7.3 GB	Medium	A81
Q5_K_M	5	8.6 GB	High	A81
Q6_KBest for your GPU	6	9.8 GB	High	A81
Q8_0	8	12.8 GB	Very High	F0
F16	16	24.6 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 3 12B on your machine.

Run

ollama run gemma3:12b

Your hardware

More models your NVIDIA A2 16GB can run

Model	Params	Grade	Decode
Qwen 3 14B	14B	S	19.7 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	18.7 tok/s
GPT-OSS 20B	21B	A	17.4 tok/s
Ministral 3 14B	14B	A	19.6 tok/s
Codestral 2 25.08	22B	B	6.8 tok/s

Frequently asked questions

Can NVIDIA A2 16GB run Gemma 3 12B?

Yes, NVIDIA A2 16GB can run Gemma 3 12B with a A grade (Tight fit). Expected decode speed: 22.4 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 12B?

The recommended quantization for Gemma 3 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 12B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Gemma 3 12B achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8652ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on NVIDIA A2 16GB receives a A grade with 22.4 tok/s and 19K context.

What context window can Gemma 3 12B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Gemma 3 12B can safely use up to 19K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 3 12B feels slow on NVIDIA A2 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA A2 16GB See all hardware for Gemma 3 12B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/gemma-3-12b-on-a2-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

22.4 tok/s

4719 ms

19K

Coding

Tight fit

22.4 tok/s

8652 ms

19K

Agentic Coding

Too heavy

10.6 tok/s

26517 ms

19K

Reasoning

Tight fit

22.4 tok/s

10225 ms

19K

RAG

Too heavy

10.6 tok/s

33146 ms

19K

Quant

Bits

VRAM

Quality

Fit

Q2_K

4.7 GB

Low

A78

Q3_K_S

5.9 GB

Low

A79

NVFP4

6.7 GB

Medium

A80

Q4_K_M

7.3 GB

Medium

A81

Q5_K_M

8.6 GB

High

A81

Q6_KBest for your GPU

9.8 GB

High

A81

Q8_0

12.8 GB

Very High

F16

24.6 GB

Maximum

Model

Params

Grade

Decode

Capabilities

Qwen 3 14B

14B

19.7 tok/s

Phi-4-reasoning-plus 14B

14.7B

18.7 tok/s

GPT-OSS 20B

21B

17.4 tok/s

Ministral 3 14B

14B

19.6 tok/s

Codestral 2 25.08

22B

6.8 tok/s

Frequently asked questions

Can NVIDIA A2 16GB run Gemma 3 12B?

Yes, NVIDIA A2 16GB can run Gemma 3 12B with a A grade (Tight fit). Expected decode speed: 22.4 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 12B?

The recommended quantization for Gemma 3 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 12B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Gemma 3 12B achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8652ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on NVIDIA A2 16GB receives a A grade with 22.4 tok/s and 19K context.

What context window can Gemma 3 12B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Gemma 3 12B can safely use up to 19K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 3 12B feels slow on NVIDIA A2 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.