Can Gemma 4 E4B run on NVIDIA A100 80GB?

YES — Runs Great

A73Great

Estimated from fit model

Gemma 4 E4B needs ~15.4 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 15.4 GB, 112.0 tok/s, Runs well

15.4 GB required80.0 GB available

19% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

128K

Memory

15.4 GB / 80.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on NVIDIA A100 80GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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	112.0 tok/s	943 ms	128K
Coding	A	Runs well	112.0 tok/s	1729 ms	128K
Agentic Coding	A	Runs well	112.0 tok/s	2514 ms	128K
Reasoning	A	Runs well	112.0 tok/s	2043 ms	128K
RAG	A	Runs well	112.0 tok/s	3143 ms	128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B67
Q3_K_S	3	3.9 GB	Low	B67
NVFP4	4	4.5 GB	Medium	B67
Q4_K_M	4	4.9 GB	Medium	B67
Q5_K_M	5	5.8 GB	High	B67
Q6_K	6	6.6 GB	High	B67
Q8_0	8	8.6 GB	Very High	B67
F16Best for your GPU	16	16.4 GB	Maximum	B68

Get started

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

Run

ollama run gemma4:e4b

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	17.6 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	259 tok/s
Qwen 3.5 27B	27B	S	112.3 tok/s
Qwen 3.6 27B	27B	S	112.7 tok/s
Qwen 3.5 122B A10B	122B	A	52.1 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Gemma 4 E4B?

Yes, NVIDIA A100 80GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 15.4 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 NVIDIA A100 80GB?

On NVIDIA A100 80GB, Gemma 4 E4B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on NVIDIA A100 80GB receives a A grade with 112.0 tok/s and 128K context.

What context window can Gemma 4 E4B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Gemma 4 E4B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Gemma 4 E4B

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