Can Gemma 4 26B A4B run on RTX 5000 Ada 32GB?

YES — Runs Great

S91Excellent

Estimated from fit model

Gemma 4 26B A4B needs ~23.4 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~75 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) — 23.4 GB, 74.8 tok/s, Runs well

23.4 GB required32.0 GB available

73% VRAM used

Fit status

Runs well

Decode

74.8 tok/s

TTFT

2587 ms

Safe context

53K

Memory

23.4 GB / 32.0 GB

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 4 26B A4B 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: 74.8 tok/s decode · 2.6s TTFT (warm) · 187 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	S	Runs well	74.8 tok/s	1411 ms	53K
Coding	S	Runs well	74.8 tok/s	2587 ms	53K
Agentic Coding	S	Tight fit	74.8 tok/s	3763 ms	53K
Reasoning	S	Runs well	74.8 tok/s	3058 ms	53K
RAG	S	Tight fit	74.8 tok/s	4704 ms	53K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	A81
Q3_K_S	3	12.3 GB	Low	A82
NVFP4	4	14.1 GB	Medium	A83
Q4_K_M	4	15.4 GB	Medium	A84
Q5_K_M	5	18.1 GB	High	A84
Q6_KBest for your GPU	6	20.7 GB	High	A84
Q8_0	8	27.0 GB	Very High	F0
F16	16	51.7 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 26B A4B on your machine.

Run

ollama run gemma4:26b

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 26B A4B?

Yes, RTX 5000 Ada 32GB can run Gemma 4 26B A4B with a S grade (Runs well). Expected decode speed: 74.8 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 23.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 26B A4B?

The recommended quantization for Gemma 4 26B A4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 26B A4B run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Gemma 4 26B A4B achieves approximately 74.8 tokens per second decode speed with a time-to-first-token of 2587ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on RTX 5000 Ada 32GB receives a S grade with 74.8 tok/s and 53K context.

What context window can Gemma 4 26B A4B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Gemma 4 26B A4B can safely use up to 53K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for Gemma 4 26B A4B

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<iframe src="https://willitrunai.com/embed/gemma-4-26b-a4b-on-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

74.8 tok/s

1411 ms

53K

Coding

Runs well

74.8 tok/s

2587 ms

53K

Agentic Coding

Tight fit

74.8 tok/s

3763 ms

53K

Reasoning

Runs well

74.8 tok/s

3058 ms

53K

RAG

Tight fit

74.8 tok/s

4704 ms

53K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	A81
Q3_K_S	3	12.3 GB	Low	A82
NVFP4	4	14.1 GB	Medium	A83
Q4_K_M	4	15.4 GB	Medium	A84
Q5_K_M	5	18.1 GB	High	A84
Q6_KBest for your GPU	6	20.7 GB	High	A84
Q8_0	8	27.0 GB	Very High	F0
F16	16	51.7 GB	Maximum	F0

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 26B A4B?

Yes, RTX 5000 Ada 32GB can run Gemma 4 26B A4B with a S grade (Runs well). Expected decode speed: 74.8 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 23.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 26B A4B?

The recommended quantization for Gemma 4 26B A4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 26B A4B run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Gemma 4 26B A4B achieves approximately 74.8 tokens per second decode speed with a time-to-first-token of 2587ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on RTX 5000 Ada 32GB receives a S grade with 74.8 tok/s and 53K context.

What context window can Gemma 4 26B A4B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Gemma 4 26B A4B can safely use up to 53K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.