Can Gemma 4 26B A4B run on RX 580 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 4 26B A4B needs ~20.7 GB but RX 580 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 20.7 GB, exceeds 8.0 GB available

20.7 GB required8.0 GB available

259% VRAM needed

12.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.7 tok/s

TTFT

72202 ms

Safe context

Memory

20.7 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 26B A4B on RX 580 8GB
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: 2.7 tok/s decode · 72.2s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 20.7 GB, but this setup only exposes 8.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.7 tok/s	39383 ms	4K
Coding	F	Too heavy	2.7 tok/s	72202 ms	4K
Agentic Coding	F	Too heavy	2.7 tok/s	105022 ms	4K
Reasoning	F	Too heavy	2.7 tok/s	85330 ms	4K
RAG	F	Too heavy	2.7 tok/s	131277 ms	4K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on RX 580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	F0
Q3_K_S	3	12.3 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Gemma 4 26B A4B well

RX 7900 XT 20GBBest value

20 GB VRAM (+12)800 GB/s (+544)

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 1681%.

~$899 MSRP

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+16)960 GB/s (+704)

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 (+24)640 GB/s (+384)

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 RX 580 8GB run Gemma 4 26B A4B?

No, Gemma 4 26B A4B requires more memory than RX 580 8GB provides.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 20.7 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 RX 580 8GB?

On RX 580 8GB, Gemma 4 26B A4B achieves approximately 2.7 tokens per second decode speed with a time-to-first-token of 72202ms using Q4_K_M quantization.

Can RX 580 8GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on RX 580 8GB receives a F grade with 2.7 tok/s and 4K context.

What context window can Gemma 4 26B A4B use on RX 580 8GB?

On RX 580 8GB, Gemma 4 26B A4B can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 26B A4B feels slow on RX 580 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RX 580 8GB See all hardware for Gemma 4 26B A4B

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