Can Gemma 4 E4B run on RTX 3080 10GB?

YES — Runs Great

A84Great

○Estimated from fit model

Gemma 4 E4B needs ~8.1 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~81 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) — 8.1 GB, 81.0 tok/s, Runs well

8.1 GB required10.0 GB available

81% VRAM used

Fit status

Runs well

Decode

81.0 tok/s

TTFT

2390 ms

Safe context

40K

Memory

8.1 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on RTX 3080 10GB
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: 81.0 tok/s decode · 2.4s TTFT (warm) · 203 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	81.0 tok/s	1304 ms	40K
Coding	A	Runs well	81.0 tok/s	2390 ms	40K
Agentic Coding	A	Tight fit	81.0 tok/s	3477 ms	40K
Reasoning	A	Runs well	81.0 tok/s	2825 ms	40K
RAG	A	Tight fit	81.0 tok/s	4346 ms	40K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A79
Q3_K_S	3	3.9 GB	Low	A80
NVFP4	4	4.5 GB	Medium	A80
Q4_K_M	4	4.9 GB	Medium	A80
Q5_K_M	5	5.8 GB	High	A80
Q6_KBest for your GPU	6	6.6 GB	High	A80
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

ollama run gemma4:e4b

Your hardware

More models your RTX 3080 10GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	91.2 tok/s
👁 NVIDIA Nemotron Nano 9B v2	9B	A	95 tok/s
👁 Tsinghua/Zhipu CodeGeeX 4 9B	9B	A	96.7 tok/s

Frequently asked questions

See all results for RTX 3080 10GB See all hardware for Gemma 4 E4B

URL: https://willitrunai.com/can-run/gemma-4-e4b-on-rtx-3080-10gb