Can Gemma 4 E4B run on RTX 4050 Laptop 6GB?

Q: What should I upgrade first if Gemma 4 E4B feels slow on RTX 4050 Laptop 6GB?

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.

YES — With Q3_K_S

B66Good

Estimated from fit model

Gemma 4 E4B needs ~7.0 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q3_K_S quantization, expect ~19 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 E4B at Q4_K_M needs 8.0 GB — too much for RTX 4050 Laptop 6GB (6.0 GB). Runs at Q3_K_S (7.0 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.0 GB, exceeds 6.0 GB available

8.0 GB required6.0 GB available

133% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.8 tok/s

TTFT

15165 ms

Safe context

Memory

8.0 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.9 GB

KV Cache1.3 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 E4B 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: 12.8 tok/s decode · 15.2s TTFT (warm) · 32 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 0.6 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	15.2 tok/s	6932 ms	4K
Coding	F	Too heavy	12.8 tok/s	15165 ms	4K
Agentic Coding	F	Too heavy	9.3 tok/s	30202 ms	4K
Reasoning	F	Too heavy	12.8 tok/s	17922 ms	4K
RAG	F	Too heavy	9.3 tok/s	37752 ms	4K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.1 GB	Low	A81
Q3_K_S	3	3.9 GB	Low	F0

Get started

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

Run

ollama run gemma4:e4b

Upgrade options

Hardware that runs Gemma 4 E4B well

RTX 3050 8GBBudget pick

8 GB VRAM (+2)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.23.4 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.

~$249 MSRP

RTX 5060 8GBBest value

8 GB VRAM (+2)448 GB/s (+256)

Makes the model fit on the accelerator instead of staying completely out of reach.44.2 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.

~$299 MSRP

RTX 4060 8GBNVIDIA upgrade

8 GB VRAM (+2)272 GB/s (+80)

Makes the model fit on the accelerator instead of staying completely out of reach.31.5 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.

~$299 MSRP

RTX 3080 Ti 12GBBiggest leap

12 GB VRAM (+6)912 GB/s (+720)

Makes the model fit on the accelerator instead of staying completely out of reach.112 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.

~$1,199 MSRP

Frequently asked questions

Can RTX 4050 Laptop 6GB run Gemma 4 E4B?

Yes, RTX 4050 Laptop 6GB can run Gemma 4 E4B at Q3_K_S quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 8.0 GB which exceeds available memory, but at Q3_K_S it needs only 7.0 GB. Expected decode speed: 19.4 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 8.0 GB at Q4_K_M quantization. On RTX 4050 Laptop 6GB, it fits at Q3_K_S using 7.0 GB.

What is the best quantization for Gemma 4 E4B?

The recommended quantization is Q4_K_M, but on RTX 4050 Laptop 6GB the best fitting quantization is Q3_K_S, which uses 7.0 GB.

What speed will Gemma 4 E4B run at on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, Gemma 4 E4B achieves approximately 19.4 tokens per second decode speed with a time-to-first-token of 9995ms using Q3_K_S quantization.

Can RTX 4050 Laptop 6GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on RTX 4050 Laptop 6GB receives a F grade with 12.8 tok/s and 4K context.

What context window can Gemma 4 E4B use on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, Gemma 4 E4B 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.

See all results for RTX 4050 Laptop 6GB See all hardware for Gemma 4 E4B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/gemma-4-e4b-on-rtx-4050-laptop-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: