Can Gemma 4 E4B run on RTX A5000 24GB?

YES — Runs Great

A78Great

Estimated from fit model

Gemma 4 E4B needs ~9.8 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~110 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) — 9.8 GB, 112.0 tok/s, Runs well

9.8 GB required24.0 GB available

41% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

128K

Memory

9.8 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 4 E4B 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: 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	110.2 tok/s	959 ms	128K
Coding	A	Runs well	110.2 tok/s	1757 ms	128K
Agentic Coding	A	Runs well	110.2 tok/s	2556 ms	128K
Reasoning	A	Runs well	110.2 tok/s	2077 ms	128K
RAG	A	Runs well	110.2 tok/s	3195 ms	128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A72
Q3_K_S	3	3.9 GB	Low	A72
NVFP4	4

Get started

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

Run

ollama run gemma4:e4b

Your hardware

More models your RTX A5000 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	81.3 tok/s
Qwen 3.5 27B	27B	S	35.3 tok/s

Frequently asked questions

Can RTX A5000 24GB run Gemma 4 E4B?

Yes, RTX A5000 24GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 110.2 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 9.8 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 A5000 24GB?

On RTX A5000 24GB, Gemma 4 E4B achieves approximately 110.2 tokens per second decode speed with a time-to-first-token of 1757ms using Q4_K_M quantization.

Can RTX A5000 24GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on RTX A5000 24GB receives a A grade with 110.2 tok/s and 128K context.

What context window can Gemma 4 E4B use on RTX A5000 24GB?

On RTX A5000 24GB, 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 RTX A5000 24GB See all hardware for Gemma 4 E4B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/gemma-4-e4b-on-a5000-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: