Can Gemma 3 12B run on RTX 4090 24GB?

YES — Runs Great

A84Great

Measured on real hardware· rtx-4090-24gb

Gemma 3 12B needs ~15.5 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~65 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 15.5 GB, 69.8 tok/s, Runs well

15.5 GB required24.0 GB available

65% VRAM used

Fit status

Runs well

Decode

69.8 tok/s

TTFT

2775 ms

Safe context

44K

Memory

15.5 GB / 24.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 3 12B on RTX 4090 24GB
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: 69.8 tok/s decode · 2.8s TTFT (warm) · 174 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	69.8 tok/s	1513 ms	44K
Coding	A	Runs well	65.0 tok/s	2937 ms	44K
Agentic Coding	A	Tight fit	69.8 tok/s	4036 ms	44K
Reasoning	A	Runs well	69.8 tok/s	3279 ms	44K
RAG	A	Tight fit	69.8 tok/s	5045 ms	44K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A75
Q3_K_S	3	5.9 GB	Low	A76
NVFP4	4	6.7 GB	Medium	A76
Q4_K_M	4	7.3 GB	Medium	A76
Q5_K_M	5	8.6 GB	High	A77
Q6_K	6	9.8 GB	High	A78
Q8_0Best for your GPU	8	12.8 GB	Very High	A80
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 RTX 4090 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	83.4 tok/s
Qwen 3.5 27B	27B	S	34.8 tok/s
Qwen 3.6 27B	27B	S	20.2 tok/s
Qwen 3.6 35B A3B	35B	A	53.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	119.8 tok/s

Frequently asked questions

Can RTX 4090 24GB run Gemma 3 12B?

Yes, RTX 4090 24GB can run Gemma 3 12B with a A grade (Runs well). Expected decode speed: 65.0 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 15.5 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 4090 24GB?

On RTX 4090 24GB, Gemma 3 12B achieves approximately 65.0 tokens per second decode speed with a time-to-first-token of 2937ms using Q4_K_M quantization.

Can RTX 4090 24GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on RTX 4090 24GB receives a A grade with 65.0 tok/s and 44K context.

What context window can Gemma 3 12B use on RTX 4090 24GB?

On RTX 4090 24GB, Gemma 3 12B can safely use up to 44K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4090 24GB See all hardware for Gemma 3 12B

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