Can Gemma 4 26B A4B run on RTX 4080 Super 16GB?

Q: What should I upgrade first if Gemma 4 26B A4B feels slow on RTX 4080 Super 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

A78Great

Estimated from fit model

Gemma 4 26B A4B needs ~18.8 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q3_K_S quantization, expect ~61 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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 21.8 GB — too much for RTX 4080 Super 16GB (16.0 GB). Runs at Q3_K_S (18.8 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 21.8 GB, exceeds 16.0 GB available

21.8 GB required16.0 GB available

136% VRAM needed

5.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

38.7 tok/s

TTFT

5007 ms

Safe context

Memory

21.8 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime1.2 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 RTX 4080 Super 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: 38.7 tok/s decode · 5.0s TTFT (warm) · 97 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 1.8 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	44.3 tok/s	2385 ms	4K
Coding	F	Too heavy	38.7 tok/s	5007 ms	4K
Agentic Coding	F	Too heavy	27.9 tok/s	10093 ms	4K
Reasoning	F	Too heavy	38.7 tok/s	5917 ms	4K
RAG	F	Too heavy	27.9 tok/s	12616 ms	4K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on RTX 4080 Super 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

RTX 4000 Ada 20GBBest value

20 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.27.4 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$1,250 MSRP

RTX 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+200)

Makes the model fit on the accelerator instead of staying completely out of reach.106.4 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.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+272)

Makes the model fit on the accelerator instead of staying completely out of reach.124.4 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.

~$1,599 MSRP

Frequently asked questions

Can RTX 4080 Super 16GB run Gemma 4 26B A4B?

Yes, RTX 4080 Super 16GB can run Gemma 4 26B A4B at Q3_K_S quantization (Very compromised (needs ~1.8 GB host RAM)). The recommended Q4_K_M requires 21.8 GB which exceeds available memory, but at Q3_K_S it needs only 18.8 GB. Expected decode speed: 61.3 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 21.8 GB at Q4_K_M quantization. On RTX 4080 Super 16GB, it fits at Q3_K_S using 18.8 GB.

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

The recommended quantization is Q4_K_M, but on RTX 4080 Super 16GB the best fitting quantization is Q3_K_S, which uses 18.8 GB.

What speed will Gemma 4 26B A4B run at on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, Gemma 4 26B A4B achieves approximately 61.3 tokens per second decode speed with a time-to-first-token of 3160ms using Q3_K_S quantization.

Can RTX 4080 Super 16GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on RTX 4080 Super 16GB receives a F grade with 38.7 tok/s and 4K context.

What context window can Gemma 4 26B A4B use on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, Gemma 4 26B A4B can safely use up to 4K 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 RTX 4080 Super 16GB See all hardware for Gemma 4 26B A4B

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