Can Gemma 3 12B run on RTX 3080 12GB?

BARELY — Tight on Memory

B64Good

Estimated from fit model

Gemma 3 12B needs ~14.3 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 14.3 GB, 34.5 tok/s, Very compromised (needs ~1.2 GB host RAM)

14.3 GB required12.0 GB available

119% VRAM needed

2.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.2 GB host RAM)

Decode

34.5 tok/s

TTFT

5618 ms

Safe context

Memory

14.3 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsGemma 3 12B on RTX 3080 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: 34.5 tok/s decode · 5.6s TTFT (warm) · 86 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.

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 1.2 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	Runs with offload	66.5 tok/s	1588 ms	8K
Coding	B	Very compromised (needs ~1.2 GB host RAM)	34.5 tok/s	5618 ms	8K
Agentic Coding	F	Too heavy	18.6 tok/s	15163 ms	8K
Reasoning	B	Very compromised (needs ~1.2 GB host RAM)	34.5 tok/s	6639 ms	8K
RAG	F	Too heavy	18.6 tok/s	18954 ms	8K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on RTX 3080 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A81
Q3_K_S	3	5.9 GB	Low	A82
NVFP4	4	6.7 GB	Medium	A82
Q4_K_M	4	7.3 GB	Medium	A82
Q5_K_MBest for your GPU	5	8.6 GB	High	A81
Q6_K	6	9.8 GB	High	F0
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

Opciones de mejora

Hardware que ejecuta bien Gemma 3 12B

RTX 5060 Ti 16GBOpción económica

16 GB VRAM (+4)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.29.3 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$449 MSRP

RTX 4060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+4)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.23 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

RTX 2000 Ada 16GBMejora NVIDIA

16 GB VRAM (+4)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.23.8 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$625 MSRP

Frequently asked questions

Can RTX 3080 12GB run Gemma 3 12B?

Yes, RTX 3080 12GB can run Gemma 3 12B with a B grade (Very compromised (needs ~1.2 GB host RAM)). Expected decode speed: 34.5 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 14.3 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 RTX 3080 12GB?

On RTX 3080 12GB, Gemma 3 12B achieves approximately 34.5 tokens per second decode speed with a time-to-first-token of 5618ms using Q4_K_M quantization.

Can RTX 3080 12GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on RTX 3080 12GB receives a B grade with 34.5 tok/s and 8K context.

What context window can Gemma 3 12B use on RTX 3080 12GB?

On RTX 3080 12GB, Gemma 3 12B can safely use up to 8K 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 RTX 3080 12GB?

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.

See all results for RTX 3080 12GB See all hardware for Gemma 3 12B

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