Can Gemma 3 12B run on RTX A4500 20GB?

YES — Runs Great

S86Excellent

Estimated from fit model

Gemma 3 12B needs ~15.4 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~68 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 15.4 GB, 71.6 tok/s, Runs well

15.4 GB required20.0 GB available

77% VRAM used

Fit status

Runs well

Decode

71.6 tok/s

TTFT

2704 ms

Safe context

31K

Memory

15.4 GB / 20.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsGemma 3 12B on RTX A4500 20GB
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: 71.6 tok/s decode · 2.7s TTFT (warm) · 179 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	71.6 tok/s	1475 ms	31K
Coding	S	Runs well	68.2 tok/s	2839 ms	31K
Agentic Coding	A	Runs with offload (needs ~0.1 GB host RAM)	52.1 tok/s	5402 ms	31K
Reasoning	S	Runs well	71.6 tok/s	3195 ms	31K
RAG	A	Runs with offload (needs ~0.1 GB host RAM)	52.1 tok/s	6753 ms

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A76
Q3_K_S	3	5.9 GB	Low	A77
NVFP4	4

Get started

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

Run

ollama run gemma3:12b

Your hardware

More models your RTX A4500 20GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	41.2 tok/s
Qwen 3.5 27B	27B	A	18.6 tok/s

Frequently asked questions

Can RTX A4500 20GB run Gemma 3 12B?

Yes, RTX A4500 20GB can run Gemma 3 12B with a S grade (Runs well). Expected decode speed: 68.2 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 15.4 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 A4500 20GB?

On RTX A4500 20GB, Gemma 3 12B achieves approximately 68.2 tokens per second decode speed with a time-to-first-token of 2839ms using Q4_K_M quantization.

Can RTX A4500 20GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on RTX A4500 20GB receives a S grade with 68.2 tok/s and 31K context.

What context window can Gemma 3 12B use on RTX A4500 20GB?

On RTX A4500 20GB, Gemma 3 12B can safely use up to 31K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX A4500 20GB See all hardware for Gemma 3 12B

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