Can Gemma 4 E4B run on GTX 1080 Ti 11GB?

YES — Runs Great

A83Great

Estimated from fit model

Gemma 4 E4B needs ~8.5 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~63 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, 62.9 tok/s, Runs well

8.5 GB required11.0 GB available

77% VRAM used

Fit status

Runs well

Decode

62.9 tok/s

TTFT

3078 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 GTX 1080 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: 62.9 tok/s decode · 3.1s TTFT (warm) · 157 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	62.9 tok/s	1679 ms	48K
Coding	A	Runs well	62.9 tok/s	3078 ms	48K
Agentic Coding	A	Tight fit	62.9 tok/s	4477 ms	48K
Reasoning	A	Runs well	62.9 tok/s	3637 ms	48K
RAG	A	Tight fit	62.9 tok/s	5596 ms	48K

Inference speed

Gemma 4 E4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 E4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	99.7	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	93.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	83.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	82.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	77.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	73.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	62.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	52.9	Fits
RTX 3060 12GB	12 GB	Q4_K_M	52.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	48.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	42.6	Fits
RTX 4060 8GB	8 GB	Q4_K_M	31.5	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on GTX 1080 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 GTX 1080 Ti 11GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	55.9 tok/s
Nemotron Nano 9B v2	9B	A	55.9 tok/s
CodeGeeX 4 9B	9B	A	56.9 tok/s

Frequently asked questions

Can GTX 1080 Ti 11GB run Gemma 4 E4B?

Yes, GTX 1080 Ti 11GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 62.9 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 GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Gemma 4 E4B achieves approximately 62.9 tokens per second decode speed with a time-to-first-token of 3078ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on GTX 1080 Ti 11GB receives a A grade with 62.9 tok/s and 48K context.

What context window can Gemma 4 E4B use on GTX 1080 Ti 11GB?

On GTX 1080 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 GTX 1080 Ti 11GB See all hardware for Gemma 4 E4B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/gemma-4-e4b-on-gtx-1080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

62.9 tok/s

1679 ms

48K

Coding

Runs well

62.9 tok/s

3078 ms

48K

Agentic Coding

Tight fit

62.9 tok/s

4477 ms

48K

Reasoning

Runs well

62.9 tok/s

3637 ms

48K

RAG

Tight fit

62.9 tok/s

5596 ms

48K

Gemma 4 E4B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

112.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

112.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

112.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

112.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

99.7

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

93.0

Fits

RTX 4070 12GB

12 GB

Q4_K_M

83.3

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

82.6

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

77.5

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

73.5

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

62.6

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

52.9

Fits

RTX 3060 12GB

12 GB

Q4_K_M

52.3

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

48.5

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

42.6

Fits

RTX 4060 8GB

8 GB

Q4_K_M

31.5

Offloads

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.1 GB

Low

A78

Q3_K_S

3.9 GB

Low

A79

NVFP4

4.5 GB

Medium

A80

Q4_K_M

4.9 GB

Medium

A80

Q5_K_M

5.8 GB

High

A80

Q6_KBest for your GPU

6.6 GB

High

A80

Q8_0

8.6 GB

Very High

F16

16.4 GB

Maximum

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

55.9 tok/s

Nemotron Nano 9B v2

55.9 tok/s

CodeGeeX 4 9B

56.9 tok/s

Frequently asked questions

Can GTX 1080 Ti 11GB run Gemma 4 E4B?

Yes, GTX 1080 Ti 11GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 62.9 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 GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Gemma 4 E4B achieves approximately 62.9 tokens per second decode speed with a time-to-first-token of 3078ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on GTX 1080 Ti 11GB receives a A grade with 62.9 tok/s and 48K context.

What context window can Gemma 4 E4B use on GTX 1080 Ti 11GB?

On GTX 1080 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.