Can Gemma 3 12B run on RTX 2080 Ti 11GB?

Q: What should I upgrade first if Gemma 3 12B feels slow on RTX 2080 Ti 11GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

B64Good

Estimated from fit model

Gemma 3 12B needs ~13.1 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q3_K_S quantization, expect ~33 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

Gemma 3 12B at Q4_K_M needs 14.5 GB — too much for RTX 2080 Ti 11GB (11.0 GB). Runs at Q3_K_S (13.1 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 14.5 GB, exceeds 11.0 GB available

14.5 GB required11.0 GB available

132% VRAM needed

3.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

22.8 tok/s

TTFT

8480 ms

Safe context

Memory

14.5 GB / 11.0 GB

Offload

20%

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 3 12B on RTX 2080 Ti 11GB
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: 22.8 tok/s decode · 8.5s TTFT (warm) · 57 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~0.6 GB host RAM)	34.2 tok/s	3086 ms	5K
Coding	F	Too heavy	22.8 tok/s	8480 ms	5K
Agentic Coding	F	Too heavy	12.1 tok/s	23329 ms	5K
Reasoning	F	Too heavy	22.8 tok/s	10022 ms	5K
RAG	F	Too heavy	12.1 tok/s	29161 ms	5K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A82
Q3_K_S	3	5.9 GB	Low	A82
NVFP4	4

Get started

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

Run

ollama run gemma3:12b

Upgrade options

Hardware that runs Gemma 3 12B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.39.8 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.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.30.2 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.

~$499 MSRP

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.31.4 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.

~$625 MSRP

Frequently asked questions

Can RTX 2080 Ti 11GB run Gemma 3 12B?

Yes, RTX 2080 Ti 11GB can run Gemma 3 12B at Q3_K_S quantization (Very compromised (needs ~0.9 GB host RAM)). The recommended Q4_K_M requires 14.5 GB which exceeds available memory, but at Q3_K_S it needs only 13.1 GB. Expected decode speed: 33.3 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 14.5 GB at Q4_K_M quantization. On RTX 2080 Ti 11GB, it fits at Q3_K_S using 13.1 GB.

What is the best quantization for Gemma 3 12B?

The recommended quantization is Q4_K_M, but on RTX 2080 Ti 11GB the best fitting quantization is Q3_K_S, which uses 13.1 GB.

What speed will Gemma 3 12B run at on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Gemma 3 12B achieves approximately 33.3 tokens per second decode speed with a time-to-first-token of 5822ms using Q3_K_S quantization.

Can RTX 2080 Ti 11GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on RTX 2080 Ti 11GB receives a F grade with 22.8 tok/s and 5K context.

What context window can Gemma 3 12B use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Gemma 3 12B can safely use up to 9K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 2080 Ti 11GB 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-rtx-2080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: