Can Gemma 4 26B A4B run on RTX 4050 Laptop 6GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Gemma 4 26B A4B needs ~20.8 GB but RTX 4050 Laptop 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very 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) — 20.8 GB, exceeds 6.0 GB available

20.8 GB required6.0 GB available

347% VRAM needed

14.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.4 tok/s

TTFT

56707 ms

Safe context

Memory

20.8 GB / 6.0 GB

Offload

70%

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 26B A4B on RTX 4050 Laptop 6GB
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: 3.4 tok/s decode · 56.7s TTFT (warm) · 9 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 20.8 GB, but this setup only exposes 6.0 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	3.4 tok/s	30931 ms	4K
Coding	F	Too heavy	3.4 tok/s	56707 ms	4K
Agentic Coding	F	Too heavy	3.4 tok/s	82483 ms	4K
Reasoning	F	Too heavy	3.4 tok/s	67017 ms	4K
RAG	F	Too heavy	3.4 tok/s	103104 ms	4K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 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	14.1 GB	Medium	F0
Q4_K_M	4	15.4 GB	Medium	F0
Q5_K_M	5	18.1 GB	High	F0
Q6_K	6	20.7 GB	High	F0
Q8_0	8	27.0 GB	Very High	F0
F16	16	51.7 GB	Maximum	F0

Upgrade options

Hardware that runs Gemma 4 26B A4B well

👁 NVIDIA
RTX 4000 Ada 20GBBest value

20 GB VRAM (+14)360 GB/s (+168)

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 706%.

~$1,250 MSRP

👁 NVIDIA
RTX 3090 24GBBudget pick

24 GB VRAM (+18)936 GB/s (+744)

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 (+18)1008 GB/s (+816)

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 4050 Laptop 6GB See all hardware for Gemma 4 26B A4B

URL: https://willitrunai.com/can-run/gemma-4-26b-a4b-on-rtx-4050-laptop-6gb

⇱ Gemma 4 26B A4B on RTX 4050 Laptop 6GB? No — Alternatives

Can Gemma 4 26B A4B run on RTX 4050 Laptop 6GB?

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