Can Gemma 4 E2B run on RTX 3050 8GB?

YES — Runs Great

A78Great

○Estimated from fit model

Gemma 4 E2B needs ~5.6 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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, 51.7 tok/s, Runs well

5.6 GB required8.0 GB available

70% VRAM used

Fit status

Runs well

Decode

51.7 tok/s

TTFT

3746 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 3050 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: 51.7 tok/s decode · 3.7s TTFT (warm) · 129 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	51.7 tok/s	2043 ms	87K
Coding	A	Runs well	51.7 tok/s	3746 ms	87K
Agentic Coding	A	Runs well	51.7 tok/s	5449 ms	87K
Reasoning	A	Runs well	51.7 tok/s	4427 ms	87K
RAG	A	Runs well	51.7 tok/s	6811 ms	87K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on RTX 3050 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	2.9 GB	Medium	A76
Q4_K_M	4	3.1 GB	Medium	A77
Q5_K_M	5	3.7 GB	High	A76
Q6_KBest for your GPU	6	4.2 GB	High	A76
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

Your hardware

More models your RTX 3050 8GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 8B	8B	A	18.7 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	19.8 tok/s
👁 InternLM InternVL2 8B	8B	A	19.8 tok/s
👁 Mistral Ministral 3 8B	8B	B	18.7 tok/s
👁 OpenBMB MiniCPM-V 2.6 8B	8B	B	19.8 tok/s

Frequently asked questions

See all results for RTX 3050 8GB See all hardware for Gemma 4 E2B

URL: https://willitrunai.com/can-run/gemma-4-e2b-on-rtx-3050-8gb