Can Gemma 3 27B run on RTX A2000 12GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 3 27B needs ~30.1 GB but RTX A2000 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 30.1 GB, exceeds 12.0 GB available

30.1 GB required12.0 GB available

251% VRAM needed

18.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.1 tok/s

TTFT

90122 ms

Safe context

Memory

30.1 GB / 12.0 GB

Offload

60%

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 3 27B on RTX A2000 12GB
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: 2.1 tok/s decode · 90.1s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 30.1 GB, but this setup only exposes 12.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.4 tok/s	44115 ms	4K
Coding	F	Too heavy	2.1 tok/s	90122 ms	4K
Agentic Coding	F	Too heavy	2.1 tok/s	131087 ms	4K
Reasoning	F	Too heavy	2.1 tok/s	106508 ms	4K
RAG	F	Too heavy	2.1 tok/s	163858 ms	4K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	F0
Q3_K_S	3	13.2 GB	Low	F0
NVFP4	4	15.1 GB	Medium	F0
Q4_K_M	4	16.5 GB	Medium	F0
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Upgrade options

Hardware that runs Gemma 3 27B well

RTX 5090 32GBBudget pick

32 GB VRAM (+20)1792 GB/s (+1504)

Makes the model fit on the accelerator instead of staying completely out of reach.58.2 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.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+20)896 GB/s (+608)

Makes the model fit on the accelerator instead of staying completely out of reach.36.5 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.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+20)576 GB/s (+288)

Makes the model fit on the accelerator instead of staying completely out of reach.21.9 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.

~$4,000 MSRP

Frequently asked questions

Can RTX A2000 12GB run Gemma 3 27B?

No, Gemma 3 27B requires more memory than RTX A2000 12GB provides.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 30.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 27B?

The recommended quantization for Gemma 3 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 27B run at on RTX A2000 12GB?

On RTX A2000 12GB, Gemma 3 27B achieves approximately 2.1 tokens per second decode speed with a time-to-first-token of 90122ms using Q4_K_M quantization.

Can RTX A2000 12GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on RTX A2000 12GB receives a F grade with 2.1 tok/s and 4K context.

What context window can Gemma 3 27B use on RTX A2000 12GB?

On RTX A2000 12GB, Gemma 3 27B can safely use up to 4K 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 27B feels slow on RTX A2000 12GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX A2000 12GB See all hardware for Gemma 3 27B

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<iframe src="https://willitrunai.com/embed/gemma-3-27b-on-a2000-12gb" 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

Too heavy

2.4 tok/s

44115 ms

Coding

Too heavy

2.1 tok/s

90122 ms

Agentic Coding

Too heavy

2.1 tok/s

131087 ms

Reasoning

Too heavy

2.1 tok/s

106508 ms

RAG

Too heavy

2.1 tok/s

163858 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

10.5 GB

Low

Q3_K_S

13.2 GB

Low

NVFP4

15.1 GB

Medium

Q4_K_M

16.5 GB

Medium

Q5_K_M

19.4 GB

High

Q6_K

22.1 GB

High

Q8_0

28.9 GB

Very High

F16

55.4 GB

Maximum

Frequently asked questions

Can RTX A2000 12GB run Gemma 3 27B?

No, Gemma 3 27B requires more memory than RTX A2000 12GB provides.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 30.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 27B?

The recommended quantization for Gemma 3 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 27B run at on RTX A2000 12GB?

On RTX A2000 12GB, Gemma 3 27B achieves approximately 2.1 tokens per second decode speed with a time-to-first-token of 90122ms using Q4_K_M quantization.

Can RTX A2000 12GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on RTX A2000 12GB receives a F grade with 2.1 tok/s and 4K context.

What context window can Gemma 3 27B use on RTX A2000 12GB?

On RTX A2000 12GB, Gemma 3 27B can safely use up to 4K 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 27B feels slow on RTX A2000 12GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.