Can Gemma 4 E4B run on RTX 3500 Ada Laptop 12GB?

YES — Runs Great

A83Great

○Estimated from fit model

Gemma 4 E4B needs ~8.6 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 8.6 GB, 54.0 tok/s, Runs well

8.6 GB required12.0 GB available

72% VRAM used

Fit status

Runs well

Decode

54.0 tok/s

TTFT

3583 ms

Safe context

59K

Memory

8.6 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on RTX 3500 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: 54.0 tok/s decode · 3.6s TTFT (warm) · 135 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	54.0 tok/s	1954 ms	59K
Coding	A	Runs well	54.0 tok/s	3583 ms	59K
Agentic Coding	A	Tight fit	54.0 tok/s	5212 ms	59K
Reasoning	A	Runs well	54.0 tok/s	4235 ms	59K
RAG	A	Tight fit	54.0 tok/s	6515 ms	59K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A77
Q3_K_S	3	3.9 GB	Low	A78
NVFP4	4	4.5 GB	Medium	A79
Q4_K_M	4	4.9 GB	Medium	A79
Q5_K_M	5	5.8 GB	High	A80
Q6_K	6	6.6 GB	High	A79
Q8_0Best for your GPU	8	8.6 GB	Very High	A79
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 E4B on your machine.

Run

ollama run gemma4:e4b

Your hardware

More models your RTX 3500 Ada Laptop 12GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	48 tok/s
👁 Alibaba Qwen 3 14B	14B	A	18.5 tok/s
👁 Mistral Ministral 3 14B	14B	A	18.4 tok/s
👁 Microsoft Phi-4 14B	14B	A	16.8 tok/s
👁 Alibaba Qwen 2.5 14B	14B	B	17.2 tok/s

Frequently asked questions

See all results for RTX 3500 Ada Laptop 12GB See all hardware for Gemma 4 E4B

URL: https://willitrunai.com/can-run/gemma-4-e4b-on-rtx-3500-ada-laptop-12gb

⇱ Gemma 4 E4B on RTX 3500 Ada Laptop 12GB? YES