Can Gemma 4 12B run on RTX 3080 12GB?

Q: What should I upgrade first if Gemma 4 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.

YES — With Q3_K_S

B68Good

Estimated from fit model

Gemma 4 12B needs ~14.1 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q3_K_S quantization, expect ~61 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: 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 4 12B at Q4_K_M needs 15.6 GB — too much for RTX 3080 12GB (12.0 GB). Runs at Q3_K_S (14.1 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 15.6 GB, exceeds 12.0 GB available

15.6 GB required12.0 GB available

130% VRAM needed

3.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

43.0 tok/s

TTFT

4498 ms

Safe context

Memory

15.6 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights7.3 GB

KV Cache5.9 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 4 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: 43.0 tok/s decode · 4.5s TTFT (warm) · 108 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 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	Runs with offload (needs ~0.4 GB host RAM)	66.7 tok/s	1582 ms	6K
Coding	F	Too heavy	41.0 tok/s	4723 ms	6K
Agentic Coding	F	Too heavy	22.0 tok/s	12811 ms	6K
Reasoning	F	Too heavy	43.0 tok/s	5316 ms	6K
RAG	F	Too heavy	22.0 tok/s	16014 ms	6K

Inference speed

Gemma 4 12B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 12B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~168 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	168.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	109.9	Fits

Quantization options

How Gemma 4 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	A83
Q3_K_S	3	5.9 GB	Low	A84
NVFP4	4

Get started

Copy-paste commands to run Gemma 4 12B on your machine.

Run

lms load gemma-4-12B-it && lms server start

Upgrade options

Hardware that runs Gemma 4 12B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+4)

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 (+4)

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 (+4)

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

RTX 3090 24GBBiggest leap

24 GB VRAM (+12)936 GB/s (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.94 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.

~$1,499 MSRP

Frequently asked questions

Can RTX 3080 12GB run Gemma 4 12B?

Yes, RTX 3080 12GB can run Gemma 4 12B at Q3_K_S quantization (Very compromised (needs ~0.9 GB host RAM)). The recommended Q4_K_M requires 15.6 GB which exceeds available memory, but at Q3_K_S it needs only 14.1 GB. Expected decode speed: 61.1 tok/s.

How much VRAM does Gemma 4 12B need?

Gemma 4 12B (12B parameters) requires approximately 15.6 GB at Q4_K_M quantization. On RTX 3080 12GB, it fits at Q3_K_S using 14.1 GB.

What is the best quantization for Gemma 4 12B?

The recommended quantization is Q4_K_M, but on RTX 3080 12GB the best fitting quantization is Q3_K_S, which uses 14.1 GB.

What speed will Gemma 4 12B run at on RTX 3080 12GB?

On RTX 3080 12GB, Gemma 4 12B achieves approximately 61.1 tokens per second decode speed with a time-to-first-token of 3168ms using Q3_K_S quantization.

Can RTX 3080 12GB run Gemma 4 12B for coding?

For coding workloads, Gemma 4 12B on RTX 3080 12GB receives a F grade with 41.0 tok/s and 6K context.

What context window can Gemma 4 12B use on RTX 3080 12GB?

On RTX 3080 12GB, Gemma 4 12B can safely use up to 10K tokens of context at Q3_K_S quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 12B feels slow on RTX 3080 12GB?

See all results for RTX 3080 12GB See all hardware for Gemma 4 12B

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