Can Gemma 3 12B run on RX 7900 XT 20GB?

YES — Runs Great

A85Great

Estimated from fit model

Gemma 3 12B needs ~15.1 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~52 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) — 15.1 GB, 52.2 tok/s, Runs well

15.1 GB required20.0 GB available

76% VRAM used

Fit status

Runs well

Decode

52.2 tok/s

TTFT

3710 ms

Safe context

32K

Memory

15.1 GB / 20.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsGemma 3 12B on RX 7900 XT 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: 52.2 tok/s decode · 3.7s TTFT (warm) · 131 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	52.2 tok/s	2024 ms	32K
Coding	A	Runs well	52.2 tok/s	3710 ms	32K
Agentic Coding	A	Runs with offload	52.2 tok/s	5397 ms	32K
Reasoning	A	Runs well	52.2 tok/s	4385 ms	32K
RAG	A	Runs with offload	52.2 tok/s	6746 ms	32K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on RX 7900 XT 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	6.7 GB	Medium	A78
Q4_K_M	4	7.3 GB	Medium	A78
Q5_K_M	5	8.6 GB	High	A79
Q6_K	6	9.8 GB	High	A80
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 RX 7900 XT 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	40.7 tok/s
Qwen 3.5 27B	27B	A	18.3 tok/s
Qwen 3.6 27B	27B	S	17.3 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	43.3 tok/s
Magistral Small 2507	24B	S	35.2 tok/s

Frequently asked questions

Can RX 7900 XT 20GB run Gemma 3 12B?

Yes, RX 7900 XT 20GB can run Gemma 3 12B with a A grade (Runs well). Expected decode speed: 52.2 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 15.1 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 RX 7900 XT 20GB?

On RX 7900 XT 20GB, Gemma 3 12B achieves approximately 52.2 tokens per second decode speed with a time-to-first-token of 3710ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on RX 7900 XT 20GB receives a A grade with 52.2 tok/s and 32K context.

What context window can Gemma 3 12B use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Gemma 3 12B can safely use up to 32K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RX 7900 XT 20GB See all hardware for Gemma 3 12B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/gemma-3-12b-on-rx-7900-xt-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

52.2 tok/s

2024 ms

32K

Coding

Runs well

52.2 tok/s

3710 ms

32K

Agentic Coding

Runs with offload

52.2 tok/s

5397 ms

32K

Reasoning

Runs well

52.2 tok/s

4385 ms

32K

RAG

Runs with offload

52.2 tok/s

6746 ms

32K

Quant

Bits

VRAM

Quality

Fit

Q2_K

4.7 GB

Low

A76

Q3_K_S

5.9 GB

Low

A77

NVFP4

6.7 GB

Medium

A78

Q4_K_M

7.3 GB

Medium

A78

Q5_K_M

8.6 GB

High

A79

Q6_K

9.8 GB

High

A80

Q8_0Best for your GPU

12.8 GB

Very High

A80

F16

24.6 GB

Maximum

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

40.7 tok/s

Qwen 3.5 27B

27B

18.3 tok/s

Qwen 3.6 27B

27B

17.3 tok/s

Qwen3-VL 30B A3B Instruct

30B

43.3 tok/s

Magistral Small 2507

24B

35.2 tok/s

Frequently asked questions

Can RX 7900 XT 20GB run Gemma 3 12B?

Yes, RX 7900 XT 20GB can run Gemma 3 12B with a A grade (Runs well). Expected decode speed: 52.2 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 15.1 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 RX 7900 XT 20GB?

On RX 7900 XT 20GB, Gemma 3 12B achieves approximately 52.2 tokens per second decode speed with a time-to-first-token of 3710ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on RX 7900 XT 20GB receives a A grade with 52.2 tok/s and 32K context.

What context window can Gemma 3 12B use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Gemma 3 12B can safely use up to 32K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.