Can Gemma 4 12B run on RTX 5000 Ada Laptop 16GB?

YES — With Offload

A84Great

Estimated from fit model

Gemma 4 12B needs ~16.0 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~60 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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) — 16.0 GB, 60.3 tok/s, Runs with offload

16.0 GB required16.0 GB available

100% VRAM used

Fit status

Runs with offload

Decode

60.3 tok/s

TTFT

3210 ms

Safe context

16K

Memory

16.0 GB / 16.0 GB

Memory breakdown

Weights7.3 GB

KV Cache5.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 4 12B on RTX 5000 Ada Laptop 16GB
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: 60.3 tok/s decode · 3.2s TTFT (warm) · 151 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	60.3 tok/s	1751 ms	16K
Coding	A	Runs with offload	60.3 tok/s	3210 ms	16K
Agentic Coding	F	Too heavy	23.5 tok/s	11983 ms	16K
Reasoning	A	Runs with offload	60.3 tok/s	3793 ms	16K
RAG	F	Too heavy	23.5 tok/s	14978 ms	16K

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
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	Offloads
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	23.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	14.8	Too big
RTX 4060 8GB	8 GB	Q4_K_M	5.5	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 4 12B (12B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A80
Q3_K_S	3	5.9 GB	Low	A81
NVFP4	4	6.7 GB	Medium	A82
Q4_K_M	4	7.3 GB	Medium	A83
Q5_K_M	5	8.6 GB	High	A83
Q6_KBest for your GPU	6	9.8 GB	High	A83
Q8_0	8	12.8 GB	Very High	F0
F16	16	24.6 GB	Maximum	F0

Get started

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

Run

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

Your hardware

More models your RTX 5000 Ada Laptop 16GB can run

Model	Params	Grade	Decode
Qwen 3 14B	14B	S	53.2 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	50.4 tok/s
GPT-OSS 20B	21B	A	47 tok/s
Ministral 3 14B	14B	S	52.9 tok/s
Codestral 2 25.08	22B	A	18.3 tok/s

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run Gemma 4 12B?

Yes, RTX 5000 Ada Laptop 16GB can run Gemma 4 12B with a A grade (Runs with offload). Expected decode speed: 60.3 tok/s.

How much VRAM does Gemma 4 12B need?

Gemma 4 12B (12B parameters) requires approximately 16.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 12B?

The recommended quantization for Gemma 4 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 12B run at on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Gemma 4 12B achieves approximately 60.3 tokens per second decode speed with a time-to-first-token of 3210ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run Gemma 4 12B for coding?

For coding workloads, Gemma 4 12B on RTX 5000 Ada Laptop 16GB receives a A grade with 60.3 tok/s and 16K context.

What context window can Gemma 4 12B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Gemma 4 12B can safely use up to 16K tokens of context. 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 5000 Ada Laptop 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 5000 Ada Laptop 16GB See all hardware for Gemma 4 12B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

60.3 tok/s

1751 ms

16K

Coding

Runs with offload

60.3 tok/s

3210 ms

16K

Agentic Coding

Too heavy

23.5 tok/s

11983 ms

16K

Reasoning

Runs with offload

60.3 tok/s

3793 ms

16K

RAG

Too heavy

23.5 tok/s

14978 ms

16K

Gemma 4 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

Offloads

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

23.5

Too big

RTX 3060 12GB

12 GB

Q4_K_M

14.8

Too big

RTX 4060 8GB

8 GB

Q4_K_M

5.5

Too big

Quantization options

How Gemma 4 12B (12B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A80
Q3_K_S	3	5.9 GB	Low	A81
NVFP4	4	6.7 GB	Medium	A82
Q4_K_M	4	7.3 GB	Medium	A83
Q5_K_M	5	8.6 GB	High	A83
Q6_KBest for your GPU	6	9.8 GB	High	A83
Q8_0	8	12.8 GB	Very High	F0
F16	16	24.6 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3 14B

14B

53.2 tok/s

Phi-4-reasoning-plus 14B

14.7B

50.4 tok/s

GPT-OSS 20B

21B

47 tok/s

Ministral 3 14B

14B

52.9 tok/s

Codestral 2 25.08

22B

18.3 tok/s

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run Gemma 4 12B?

Yes, RTX 5000 Ada Laptop 16GB can run Gemma 4 12B with a A grade (Runs with offload). Expected decode speed: 60.3 tok/s.

How much VRAM does Gemma 4 12B need?

Gemma 4 12B (12B parameters) requires approximately 16.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 12B?

The recommended quantization for Gemma 4 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 12B run at on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Gemma 4 12B achieves approximately 60.3 tokens per second decode speed with a time-to-first-token of 3210ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run Gemma 4 12B for coding?

For coding workloads, Gemma 4 12B on RTX 5000 Ada Laptop 16GB receives a A grade with 60.3 tok/s and 16K context.

What context window can Gemma 4 12B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Gemma 4 12B can safely use up to 16K tokens of context. 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 5000 Ada Laptop 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.