Can Gemma 4 26B A4B run on RX 9060 XT 16GB?

YES — With Q3_K_S

A74Great

○Estimated from fit model

Gemma 4 26B A4B needs ~18.5 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q3_K_S quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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.

Gemma 4 26B A4B at Q4_K_M needs 21.5 GB — too much for RX 9060 XT 16GB (16.0 GB). Runs at Q3_K_S (18.5 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 21.5 GB, exceeds 16.0 GB available

21.5 GB required16.0 GB available

134% VRAM needed

5.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.1 tok/s

TTFT

14735 ms

Safe context

Memory

21.5 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 26B A4B on RX 9060 XT 16GB
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: 13.1 tok/s decode · 14.7s TTFT (warm) · 33 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	15.8 tok/s	6666 ms	4K
Coding	F	Too heavy	12.5 tok/s	15472 ms	4K
Agentic Coding	F	Too heavy	9.4 tok/s	29831 ms	4K
Reasoning	F	Too heavy	13.1 tok/s	17414 ms	4K
RAG	F	Too heavy	9.4 tok/s	37289 ms	4K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	9.8 GB	Low	S86
Q3_K_S	3	12.3 GB	Low	F0

Get started

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

Run

ollama run gemma4:26b

Upgrade options

Hardware that runs Gemma 4 26B A4B well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+480)

Makes the model fit on the accelerator instead of staying completely out of reach.48.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 267%.

~$899 MSRP

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+8)960 GB/s (+640)

Makes the model fit on the accelerator instead of staying completely out of reach.112.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)640 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.61.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Frequently asked questions

See all results for RX 9060 XT 16GB See all hardware for Gemma 4 26B A4B

URL: https://willitrunai.com/can-run/gemma-4-26b-a4b-on-rx-9060-xt-16gb

⇱

Can Gemma 4 26B A4B run on RX 9060 XT 16GB?

YES — With Q3_K_S

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Gemma 4 26B A4B well

Frequently asked questions