Can Gemma 4 E2B run on RTX 4090 Laptop 16GB?

YES — Runs Great

A73Great

Estimated from fit model

Gemma 4 E2B needs ~6.1 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~82 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 6.1 GB, 81.6 tok/s, Runs well

6.1 GB required16.0 GB available

38% VRAM used

Fit status

Runs well

Decode

81.6 tok/s

TTFT

2373 ms

Safe context

128K

Memory

6.1 GB / 16.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on RTX 4090 Laptop 16GB
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: 81.6 tok/s decode · 2.4s TTFT (warm) · 204 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	81.6 tok/s	1294 ms	128K
Coding	A	Runs well	81.6 tok/s	2373 ms	128K
Agentic Coding	A	Runs well	81.6 tok/s	3451 ms	128K
Reasoning	A	Runs well	81.6 tok/s	2804 ms	128K
RAG	A	Runs well	81.6 tok/s	4314 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	B69
Q3_K_S	3	2.5 GB	Low	B69
NVFP4	4	2.9 GB	Medium	B70
Q4_K_M	4	3.1 GB	Medium	B70
Q5_K_M	5	3.7 GB	High	A70
Q6_K	6	4.2 GB	High	A71
Q8_0	8	5.5 GB	Very High	A72
F16Best for your GPU	16	10.5 GB	Maximum	A74

Get started

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

Run

ollama run gemma4:e2b

Your hardware

More models your RTX 4090 Laptop 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	87.2 tok/s
Qwen 3 14B	14B	S	66.7 tok/s
Qwen 3 8B	8B	S	98.1 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	56.9 tok/s
GPT-OSS 20B	21B	A	48 tok/s

Frequently asked questions

Can RTX 4090 Laptop 16GB run Gemma 4 E2B?

Yes, RTX 4090 Laptop 16GB can run Gemma 4 E2B with a A grade (Runs well). Expected decode speed: 81.6 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 6.1 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 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Gemma 4 E2B achieves approximately 81.6 tokens per second decode speed with a time-to-first-token of 2373ms using Q4_K_M quantization.

Can RTX 4090 Laptop 16GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on RTX 4090 Laptop 16GB receives a A grade with 81.6 tok/s and 128K context.

What context window can Gemma 4 E2B use on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Gemma 4 E2B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 4090 Laptop 16GB See all hardware for Gemma 4 E2B

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