Can Gemma 4 26B A4B run on RTX 4000 Ada Laptop 12GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Gemma 4 26B A4B needs ~21.4 GB but RTX 4000 Ada Laptop 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 21.4 GB, exceeds 12.0 GB available

21.4 GB required12.0 GB available

178% VRAM needed

9.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

11.3 tok/s

TTFT

17092 ms

Safe context

Memory

21.4 GB / 12.0 GB

Offload

40%

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 26B A4B on RTX 4000 Ada Laptop 12GB
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: 11.3 tok/s decode · 17.1s TTFT (warm) · 28 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 21.4 GB, but this setup only exposes 12.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	13.0 tok/s	8111 ms	4K
Coding	F	Too heavy	10.8 tok/s	17946 ms	4K
Agentic Coding	F	Too heavy	7.7 tok/s	36383 ms	4K
Reasoning	F	Too heavy	10.8 tok/s	21209 ms	4K
RAG	F	Too heavy	7.7 tok/s	45479 ms	4K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	F0
Q3_K_S	3	12.3 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Gemma 4 26B A4B well

👁 NVIDIA
RTX 4000 Ada 20GBBest value

20 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.27.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 142%.

~$1,250 MSRP

👁 NVIDIA
RTX 3090 24GBBudget pick

24 GB VRAM (+12)936 GB/s (+504)

Makes the model fit on the accelerator instead of staying completely out of reach.106.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+12)1008 GB/s (+576)

Makes the model fit on the accelerator instead of staying completely out of reach.124.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

See all results for RTX 4000 Ada Laptop 12GB See all hardware for Gemma 4 26B A4B

URL: https://willitrunai.com/can-run/gemma-4-26b-a4b-on-rtx-4000-ada-laptop-12gb

⇱

Can Gemma 4 26B A4B run on RTX 4000 Ada Laptop 12GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Gemma 4 26B A4B well

Frequently asked questions