Can Gemma 4 E4B run on RTX 4500 Ada 24GB?

YES — Runs Great

A77Great

Estimated from fit model

Gemma 4 E4B needs ~9.8 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~75 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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, 75.2 tok/s, Runs well

9.8 GB required24.0 GB available

41% VRAM used

Fit status

Runs well

Decode

75.2 tok/s

TTFT

2575 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 4500 Ada 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: 75.2 tok/s decode · 2.6s TTFT (warm) · 188 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	75.2 tok/s	1405 ms	128K
Coding	A	Runs well	75.2 tok/s	2575 ms	128K
Agentic Coding	A	Runs well	75.2 tok/s	3746 ms	128K
Reasoning	A	Runs well	75.2 tok/s	3043 ms	128K
RAG	A	Runs well	75.2 tok/s	4682 ms	128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 4500 Ada 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	4.5 GB	Medium	A73
Q4_K_M	4	4.9 GB	Medium	A73
Q5_K_M	5	5.8 GB	High	A73
Q6_K	6	6.6 GB	High	A74
Q8_0	8	8.6 GB	Very High	A75
F16Best for your GPU	16	16.4 GB	Maximum	A77

Get started

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

Run

ollama run gemma4:e4b

Your hardware

More models your RTX 4500 Ada 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	51.6 tok/s
Qwen 3.5 27B	27B	S	22.4 tok/s
Qwen 3.6 27B	27B	S	22.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	53.4 tok/s
Qwen 3.5 9B	9B	S	66.8 tok/s

Frequently asked questions

Can RTX 4500 Ada 24GB run Gemma 4 E4B?

Yes, RTX 4500 Ada 24GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 75.2 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 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Gemma 4 E4B achieves approximately 75.2 tokens per second decode speed with a time-to-first-token of 2575ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on RTX 4500 Ada 24GB receives a A grade with 75.2 tok/s and 128K context.

What context window can Gemma 4 E4B use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 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 4500 Ada 24GB See all hardware for Gemma 4 E4B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

75.2 tok/s

1405 ms

128K

Coding

Runs well

75.2 tok/s

2575 ms

128K

Agentic Coding

Runs well

75.2 tok/s

3746 ms

128K

Reasoning

Runs well

75.2 tok/s

3043 ms

128K

RAG

Runs well

75.2 tok/s

4682 ms

128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 4500 Ada 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	4.5 GB	Medium	A73
Q4_K_M	4	4.9 GB	Medium	A73
Q5_K_M	5	5.8 GB	High	A73
Q6_K	6	6.6 GB	High	A74
Q8_0	8	8.6 GB	Very High	A75
F16Best for your GPU	16	16.4 GB	Maximum	A77

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

51.6 tok/s

Qwen 3.5 27B

27B

22.4 tok/s

Qwen 3.6 27B

27B

22.4 tok/s

Qwen3-VL 30B A3B Instruct

30B

53.4 tok/s

Qwen 3.5 9B

66.8 tok/s

Frequently asked questions

Can RTX 4500 Ada 24GB run Gemma 4 E4B?

Yes, RTX 4500 Ada 24GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 75.2 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 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Gemma 4 E4B achieves approximately 75.2 tokens per second decode speed with a time-to-first-token of 2575ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on RTX 4500 Ada 24GB receives a A grade with 75.2 tok/s and 128K context.

What context window can Gemma 4 E4B use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 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.