Can Gemma 4 E2B run on RTX A5000 24GB?

YES — Runs Great

A70Great

Estimated from fit model

Gemma 4 E2B needs ~7.2 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~71 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) — 7.2 GB, 71.4 tok/s, Runs well

7.2 GB required24.0 GB available

30% VRAM used

Fit status

Runs well

Decode

71.4 tok/s

TTFT

2711 ms

Safe context

128K

Memory

7.2 GB / 24.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on RTX A5000 24GB
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: 71.4 tok/s decode · 2.7s TTFT (warm) · 179 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	71.4 tok/s	1479 ms	128K
Coding	A	Runs well	71.4 tok/s	2711 ms	128K
Agentic Coding	A	Runs well	71.4 tok/s	3944 ms	128K
Reasoning	A	Runs well	71.4 tok/s	3204 ms	128K
RAG	A	Runs well	71.4 tok/s	4930 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	B67
Q3_K_S	3	2.5 GB	Low	B67
NVFP4	4	2.9 GB	Medium	B67
Q4_K_M	4	3.1 GB	Medium	B67
Q5_K_M	5	3.7 GB	High	B68
Q6_K	6	4.2 GB	High	B68
Q8_0	8	5.5 GB	Very High	B69
F16Best for your GPU	16	10.5 GB	Maximum	A72

Get started

Copy-paste commands to run Gemma 4 E2B on your machine.

Run

ollama run gemma4:e2b

Your hardware

More models your RTX A5000 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	81.3 tok/s
Qwen 3.5 27B	27B	S	35.3 tok/s
Qwen 3.6 27B	27B	S	35.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	84.1 tok/s
Qwen 3.5 9B	9B	S	105.3 tok/s

Frequently asked questions

Can RTX A5000 24GB run Gemma 4 E2B?

Yes, RTX A5000 24GB can run Gemma 4 E2B with a A grade (Runs well). Expected decode speed: 71.4 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 7.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E2B?

The recommended quantization for Gemma 4 E2B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E2B run at on RTX A5000 24GB?

On RTX A5000 24GB, Gemma 4 E2B achieves approximately 71.4 tokens per second decode speed with a time-to-first-token of 2711ms using Q4_K_M quantization.

Can RTX A5000 24GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on RTX A5000 24GB receives a A grade with 71.4 tok/s and 128K context.

What context window can Gemma 4 E2B use on RTX A5000 24GB?

On RTX A5000 24GB, Gemma 4 E2B 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 RTX A5000 24GB See all hardware for Gemma 4 E2B

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