Can Gemma 4 26B A4B run on Radeon RX 7900M 16GB?

Q: What should I upgrade first if Gemma 4 26B A4B feels slow on Radeon RX 7900M 16GB?

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.

YES — With Q3_K_S

A76Great

Estimated from fit model

Gemma 4 26B A4B needs ~18.5 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q3_K_S quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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 Radeon RX 7900M 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

22.1 tok/s

TTFT

8741 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 Radeon RX 7900M 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: 22.1 tok/s decode · 8.7s TTFT (warm) · 55 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	26.7 tok/s	3955 ms	4K
Coding	F	Too heavy	21.1 tok/s	9178 ms	4K
Agentic Coding	F	Too heavy	15.9 tok/s	17696 ms	4K
Reasoning	F	Too heavy	22.1 tok/s	10331 ms	4K
RAG	F	Too heavy	15.9 tok/s	22120 ms	4K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on Radeon RX 7900M 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 (+224)

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 118%.

~$899 MSRP

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+8)960 GB/s (+384)

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 (+64)

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

Can Radeon RX 7900M 16GB run Gemma 4 26B A4B?

Yes, Radeon RX 7900M 16GB can run Gemma 4 26B A4B at Q3_K_S quantization (Very compromised (needs ~1.7 GB host RAM)). The recommended Q4_K_M requires 21.5 GB which exceeds available memory, but at Q3_K_S it needs only 18.5 GB. Expected decode speed: 35.3 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 21.5 GB at Q4_K_M quantization. On Radeon RX 7900M 16GB, it fits at Q3_K_S using 18.5 GB.

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

The recommended quantization is Q4_K_M, but on Radeon RX 7900M 16GB the best fitting quantization is Q3_K_S, which uses 18.5 GB.

What speed will Gemma 4 26B A4B run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, Gemma 4 26B A4B achieves approximately 35.3 tokens per second decode speed with a time-to-first-token of 5491ms using Q3_K_S quantization.

Can Radeon RX 7900M 16GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on Radeon RX 7900M 16GB receives a F grade with 21.1 tok/s and 4K context.

What context window can Gemma 4 26B A4B use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, Gemma 4 26B A4B can safely use up to 5K tokens of context at Q3_K_S quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for Radeon RX 7900M 16GB See all hardware for Gemma 4 26B A4B

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