Can Gemma 4 26B A4B run on RTX A5500 24GB?

YES — Tight Fit

S89Excellent

○Estimated from fit model

Gemma 4 26B A4B needs ~22.6 GB VRAM. RTX A5500 24GB has 24.0 GB. With Q4_K_M quantization, expect ~97 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 22.6 GB, 97.3 tok/s, Tight fit

22.6 GB required24.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

97.3 tok/s

TTFT

1990 ms

Safe context

22K

Memory

22.6 GB / 24.0 GB

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 4 26B A4B on RTX A5500 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: 97.3 tok/s decode · 2.0s TTFT (warm) · 243 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	97.3 tok/s	1086 ms	22K
Coding	S	Tight fit	97.3 tok/s	1990 ms	22K
Agentic Coding	A	Very compromised (needs ~1.3 GB host RAM)	60.2 tok/s	4678 ms	22K
Reasoning	S	Tight fit	97.3 tok/s	2352 ms	22K
RAG	A	Very compromised (needs ~1.3 GB host RAM)	60.2 tok/s	5848 ms	22K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on RTX A5500 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	A84
Q3_K_S	3	12.3 GB	Low	S85
NVFP4	4	14.1 GB	Medium	S85
Q4_K_M	4	15.4 GB	Medium	A85
Q5_K_MBest for your GPU	5	18.1 GB	High	A84
Q6_K	6	20.7 GB	High	F0
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 A5500 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	90.6 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	39.3 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	39.4 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	93.7 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	50.7 tok/s

Frequently asked questions

See all results for RTX A5500 24GB See all hardware for Gemma 4 26B A4B

URL: https://willitrunai.com/can-run/gemma-4-26b-a4b-on-rtx-a5500-24gb

⇱ Gemma 4 26B A4B on RTX A5500 24GB? TIGHT FIT