Can Gemma 3 12B run on Radeon Pro W7900 48GB?

YES — Runs Great

A79Great

Estimated from fit model

Gemma 3 12B needs ~18.2 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~73 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 18.2 GB, 73.1 tok/s, Runs well

18.2 GB required48.0 GB available

38% VRAM used

Fit status

Runs well

Decode

73.1 tok/s

TTFT

2648 ms

Safe context

114K

Memory

18.2 GB / 48.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsGemma 3 12B on Radeon Pro W7900 48GB
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: 73.1 tok/s decode · 2.6s TTFT (warm) · 183 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	73.1 tok/s	1444 ms	114K
Coding	A	Runs well	73.1 tok/s	2648 ms	114K
Agentic Coding	A	Runs well	73.1 tok/s	3851 ms	114K
Reasoning	A	Runs well	73.1 tok/s	3129 ms	114K
RAG	A	Runs well	73.1 tok/s	4814 ms	114K

Inference speed

Gemma 3 12B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 3 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
RX 7900 XTX 24GB	24 GB	Q4_K_M	99.1	Fits
RTX 3090 24GB	24 GB	Q4_K_M	94.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	87.6	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	60.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	50.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	47.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	43.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	34.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	32.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	31.6	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	26.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	21.5	Heavy offload
RTX 3060 12GB	12 GB	Q4_K_M	13.1	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	6.4	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A71
Q3_K_S	3	5.9 GB	Low	A72
NVFP4	4	6.7 GB	Medium	A72
Q4_K_M	4	7.3 GB	Medium	A72
Q5_K_M	5	8.6 GB	High	A72
Q6_K	6	9.8 GB	High	A72
Q8_0	8	12.8 GB	Very High	A73
F16Best for your GPU	16	24.6 GB	Maximum	A77

Get started

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

Run

ollama run gemma3:12b

Your hardware

More models your Radeon Pro W7900 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	77.1 tok/s
Qwen 3.5 27B	27B	S	33.4 tok/s
Qwen 3.6 27B	27B	S	33.5 tok/s
Qwen 3.6 35B A3B	35B	S	64.8 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	79.7 tok/s

Frequently asked questions

Can Radeon Pro W7900 48GB run Gemma 3 12B?

Yes, Radeon Pro W7900 48GB can run Gemma 3 12B with a A grade (Runs well). Expected decode speed: 73.1 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 18.2 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 Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Gemma 3 12B achieves approximately 73.1 tokens per second decode speed with a time-to-first-token of 2648ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on Radeon Pro W7900 48GB receives a A grade with 73.1 tok/s and 114K context.

What context window can Gemma 3 12B use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Gemma 3 12B can safely use up to 114K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon Pro W7900 48GB See all hardware for Gemma 3 12B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

73.1 tok/s

1444 ms

114K

Coding

Runs well

73.1 tok/s

2648 ms

114K

Agentic Coding

Runs well

73.1 tok/s

3851 ms

114K

Reasoning

Runs well

73.1 tok/s

3129 ms

114K

RAG

Runs well

73.1 tok/s

4814 ms

114K

Gemma 3 12B inference speed — tokens per second by GPU & Mac

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

RX 7900 XTX 24GB

24 GB

Q4_K_M

99.1

Fits

RTX 3090 24GB

24 GB

Q4_K_M

94.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

87.6

Tight

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

60.5

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

50.4

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

47.8

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

43.4

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

34.4

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

32.9

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

31.6

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

26.5

Fits

RTX 4070 12GB

12 GB

Q4_K_M

21.5

Heavy offload

RTX 3060 12GB

12 GB

Q4_K_M

13.1

Heavy offload

RTX 4060 8GB

8 GB

Q4_K_M

6.4

Too big

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A71
Q3_K_S	3	5.9 GB	Low	A72
NVFP4	4	6.7 GB	Medium	A72
Q4_K_M	4	7.3 GB	Medium	A72
Q5_K_M	5	8.6 GB	High	A72
Q6_K	6	9.8 GB	High	A72
Q8_0	8	12.8 GB	Very High	A73
F16Best for your GPU	16	24.6 GB	Maximum	A77

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

77.1 tok/s

Qwen 3.5 27B

27B

33.4 tok/s

Qwen 3.6 27B

27B

33.5 tok/s

Qwen 3.6 35B A3B

35B

64.8 tok/s

Qwen3-VL 30B A3B Instruct

30B

79.7 tok/s

Frequently asked questions

Can Radeon Pro W7900 48GB run Gemma 3 12B?

Yes, Radeon Pro W7900 48GB can run Gemma 3 12B with a A grade (Runs well). Expected decode speed: 73.1 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 18.2 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 Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Gemma 3 12B achieves approximately 73.1 tokens per second decode speed with a time-to-first-token of 2648ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on Radeon Pro W7900 48GB receives a A grade with 73.1 tok/s and 114K context.

What context window can Gemma 3 12B use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Gemma 3 12B can safely use up to 114K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.