Can Gemma 4 E2B run on RTX 3050 Ti Laptop 4GB?

YES — With Q3_K_S

B63Good

Estimated from fit model

Gemma 4 E2B needs ~4.6 GB VRAM. RTX 3050 Ti Laptop 4GB has 4.0 GB. With Q3_K_S quantization, expect ~33 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Host offload

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.

Gemma 4 E2B at Q4_K_M needs 5.2 GB — too much for RTX 3050 Ti Laptop 4GB (4.0 GB). Runs at Q3_K_S (4.6 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 5.2 GB, exceeds 4.0 GB available

5.2 GB required4.0 GB available

130% VRAM needed

1.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

22.2 tok/s

TTFT

8723 ms

Safe context

Memory

5.2 GB / 4.0 GB

Offload

20%

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 E2B on RTX 3050 Ti Laptop 4GB
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: 22.2 tok/s decode · 8.7s TTFT (warm) · 56 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	24.8 tok/s	4262 ms	4K
Coding	F	Too heavy	22.2 tok/s	8723 ms	4K
Agentic Coding	F	Too heavy	18.1 tok/s	15561 ms	4K
Reasoning	F	Too heavy	22.2 tok/s	10309 ms	4K
RAG	F	Too heavy	18.1 tok/s	19451 ms	4K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	F0
Q3_K_S	3	2.5 GB	Low	F0
NVFP4	4	2.9 GB	Medium	F0
Q4_K_M	4	3.1 GB	Medium	F0
Q5_K_M	5	3.7 GB	High	F0
Q6_K	6	4.2 GB	High	F0
Q8_0	8	5.5 GB	Very High	F0
F16	16	10.5 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 E2B on your machine.

Run

ollama run gemma4:e2b

アップグレードオプション

Gemma 4 E2Bを快適に動かすハードウェア

GTX 1660 Super 6GBコスパ重視

6 GB VRAM (+2)336 GB/s (+144)

Makes the model fit on the accelerator instead of staying completely out of reach.64.6 tok/sデコード

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.

〜$229 MSRP

RTX 3050 8GBコスパ最良

8 GB VRAM (+4)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.51.7 tok/sデコード

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.

〜$249 MSRP

GTX 1060 6GBNVIDIAアップグレード

6 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.39.6 tok/sデコード

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.

〜$249 MSRP

Frequently asked questions

Can RTX 3050 Ti Laptop 4GB run Gemma 4 E2B?

Yes, RTX 3050 Ti Laptop 4GB can run Gemma 4 E2B at Q3_K_S quantization (Very compromised (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 5.2 GB which exceeds available memory, but at Q3_K_S it needs only 4.6 GB. Expected decode speed: 33.4 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 5.2 GB at Q4_K_M quantization. On RTX 3050 Ti Laptop 4GB, it fits at Q3_K_S using 4.6 GB.

What is the best quantization for Gemma 4 E2B?

The recommended quantization is Q4_K_M, but on RTX 3050 Ti Laptop 4GB the best fitting quantization is Q3_K_S, which uses 4.6 GB.

What speed will Gemma 4 E2B run at on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, Gemma 4 E2B achieves approximately 33.4 tokens per second decode speed with a time-to-first-token of 5803ms using Q3_K_S quantization.

Can RTX 3050 Ti Laptop 4GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on RTX 3050 Ti Laptop 4GB receives a F grade with 22.2 tok/s and 4K context.

What context window can Gemma 4 E2B use on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, Gemma 4 E2B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 E2B feels slow on RTX 3050 Ti Laptop 4GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 3050 Ti Laptop 4GB See all hardware for Gemma 4 E2B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/gemma-4-e2b-on-rtx-3050-ti-laptop-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: