Can Gemma 4 E2B run on RTX 4060 Laptop 8GB?

YES — Runs Great

A78Great

Estimated from fit model

Gemma 4 E2B needs ~5.6 GB VRAM. RTX 4060 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~62 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) — 5.6 GB, 67.1 tok/s, Runs well

5.6 GB required8.0 GB available

70% VRAM used

Fit status

Runs well

Decode

67.1 tok/s

TTFT

2885 ms

Safe context

87K

Memory

5.6 GB / 8.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on RTX 4060 Laptop 8GB
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: 67.1 tok/s decode · 2.9s TTFT (warm) · 168 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	67.1 tok/s	1573 ms	87K
Coding	A	Runs well	61.7 tok/s	3137 ms	87K
Agentic Coding	A	Runs well	67.1 tok/s	4196 ms	87K
Reasoning	A	Runs well	67.1 tok/s	3409 ms	87K
RAG	A	Runs well	67.1 tok/s	5245 ms	87K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on RTX 4060 Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	A75
Q3_K_S	3	2.5 GB	Low	A76
NVFP4	4

Get started

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

Run

ollama run gemma4:e2b

Your hardware

More models your RTX 4060 Laptop 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3 8B	8B	A	24.3 tok/s
Nemotron Nano 8B	8B	A	25.8 tok/s

Frequently asked questions

Can RTX 4060 Laptop 8GB run Gemma 4 E2B?

Yes, RTX 4060 Laptop 8GB can run Gemma 4 E2B with a A grade (Runs well). Expected decode speed: 61.7 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 5.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E2B?

The recommended quantization for Gemma 4 E2B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E2B run at on RTX 4060 Laptop 8GB?

On RTX 4060 Laptop 8GB, Gemma 4 E2B achieves approximately 61.7 tokens per second decode speed with a time-to-first-token of 3137ms using Q4_K_M quantization.

Can RTX 4060 Laptop 8GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on RTX 4060 Laptop 8GB receives a A grade with 61.7 tok/s and 87K context.

What context window can Gemma 4 E2B use on RTX 4060 Laptop 8GB?

On RTX 4060 Laptop 8GB, Gemma 4 E2B can safely use up to 87K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 4060 Laptop 8GB See all hardware for Gemma 4 E2B

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