Can Gemma 4 E4B run on RTX 5000 Ada 32GB?

YES — Runs Great

A76Great

Estimated from fit model

Gemma 4 E4B needs ~10.6 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~102 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) — 10.6 GB, 101.5 tok/s, Runs well

10.6 GB required32.0 GB available

33% VRAM used

Fit status

Runs well

Decode

101.5 tok/s

TTFT

1907 ms

Safe context

128K

Memory

10.6 GB / 32.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on RTX 5000 Ada 32GB
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: 101.5 tok/s decode · 1.9s TTFT (warm) · 254 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	101.5 tok/s	1040 ms	128K
Coding	A	Runs well	101.5 tok/s	1907 ms	128K
Agentic Coding	A	Runs well	101.5 tok/s	2774 ms	128K
Reasoning	A	Runs well	101.5 tok/s	2254 ms	128K
RAG	A	Runs well	101.5 tok/s	3468 ms	128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A70
Q3_K_S	3	3.9 GB	Low	A71
NVFP4	4	4.5 GB	Medium	A71
Q4_K_M	4	4.9 GB	Medium	A71
Q5_K_M	5	5.8 GB	High	A71
Q6_K	6	6.6 GB	High	A72
Q8_0	8	8.6 GB	Very High	A72
F16Best for your GPU	16	16.4 GB	Maximum	A76

Get started

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

Run

ollama run gemma4:e4b

Your hardware

More models your RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	69.7 tok/s
Qwen 3.5 27B	27B	S	30.2 tok/s
Qwen 3.6 27B	27B	S	30.3 tok/s
Qwen 3.6 35B A3B	35B	S	58.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	72.1 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run Gemma 4 E4B?

Yes, RTX 5000 Ada 32GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 101.5 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 10.6 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 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Gemma 4 E4B achieves approximately 101.5 tokens per second decode speed with a time-to-first-token of 1907ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on RTX 5000 Ada 32GB receives a A grade with 101.5 tok/s and 128K context.

What context window can Gemma 4 E4B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, 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 5000 Ada 32GB See all hardware for Gemma 4 E4B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

101.5 tok/s

1040 ms

128K

Coding

Runs well

101.5 tok/s

1907 ms

128K

Agentic Coding

Runs well

101.5 tok/s

2774 ms

128K

Reasoning

Runs well

101.5 tok/s

2254 ms

128K

RAG

Runs well

101.5 tok/s

3468 ms

128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A70
Q3_K_S	3	3.9 GB	Low	A71
NVFP4	4	4.5 GB	Medium	A71
Q4_K_M	4	4.9 GB	Medium	A71
Q5_K_M	5	5.8 GB	High	A71
Q6_K	6	6.6 GB	High	A72
Q8_0	8	8.6 GB	Very High	A72
F16Best for your GPU	16	16.4 GB	Maximum	A76

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

69.7 tok/s

Qwen 3.5 27B

27B

30.2 tok/s

Qwen 3.6 27B

27B

30.3 tok/s

Qwen 3.6 35B A3B

35B

58.6 tok/s

Qwen3-VL 30B A3B Instruct

30B

72.1 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run Gemma 4 E4B?

Yes, RTX 5000 Ada 32GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 101.5 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 10.6 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 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Gemma 4 E4B achieves approximately 101.5 tokens per second decode speed with a time-to-first-token of 1907ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on RTX 5000 Ada 32GB receives a A grade with 101.5 tok/s and 128K context.

What context window can Gemma 4 E4B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, 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.