Can Gemma 4 E4B run on RTX 2080 Ti 11GB?

YES — Runs Great

A84Great

Estimated from fit model

Gemma 4 E4B needs ~8.5 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~88 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 8.5 GB, 88.2 tok/s, Runs well

8.5 GB required11.0 GB available

77% VRAM used

Fit status

Runs well

Decode

88.2 tok/s

TTFT

2195 ms

Safe context

48K

Memory

8.5 GB / 11.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on RTX 2080 Ti 11GB
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: 88.2 tok/s decode · 2.2s TTFT (warm) · 221 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	88.2 tok/s	1197 ms	48K
Coding	A	Runs well	88.2 tok/s	2195 ms	48K
Agentic Coding	A	Tight fit	88.2 tok/s	3193 ms	48K
Reasoning	A	Runs well	88.2 tok/s	2594 ms	48K
RAG	A	Tight fit	88.2 tok/s	3991 ms	48K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A78
Q3_K_S	3	3.9 GB	Low	A79
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 2080 Ti 11GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	78.4 tok/s
Nemotron Nano 9B v2	9B	A	78.4 tok/s
CodeGeeX 4 9B	9B	A	79.8 tok/s

Frequently asked questions

Can RTX 2080 Ti 11GB run Gemma 4 E4B?

Yes, RTX 2080 Ti 11GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 88.2 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E4B?

The recommended quantization for Gemma 4 E4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E4B run at on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Gemma 4 E4B achieves approximately 88.2 tokens per second decode speed with a time-to-first-token of 2195ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on RTX 2080 Ti 11GB receives a A grade with 88.2 tok/s and 48K context.

What context window can Gemma 4 E4B use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Gemma 4 E4B can safely use up to 48K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 2080 Ti 11GB See all hardware for Gemma 4 E4B

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