Can Gemma 4 E4B run on RX 7900 XT 20GB?

YES — Runs Great

A79Great

Estimated from fit model

Gemma 4 E4B needs ~9.1 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~80 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 9.1 GB, 80.1 tok/s, Runs well

9.1 GB required20.0 GB available

45% VRAM used

Fit status

Runs well

Decode

80.1 tok/s

TTFT

2416 ms

Safe context

128K

Memory

9.1 GB / 20.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on RX 7900 XT 20GB
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: 80.1 tok/s decode · 2.4s TTFT (warm) · 200 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	80.1 tok/s	1318 ms	128K
Coding	A	Runs well	80.1 tok/s	2416 ms	128K
Agentic Coding	A	Runs well	80.1 tok/s	3514 ms	128K
Reasoning	A	Runs well	80.1 tok/s	2855 ms	128K
RAG	A	Runs well	80.1 tok/s	4393 ms	128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A73
Q3_K_S	3	3.9 GB	Low	A73
NVFP4	4

Get started

Copy-paste commands to run Gemma 4 E4B on your machine.

Run

ollama run gemma4:e4b

Your hardware

More models your RX 7900 XT 20GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	40.7 tok/s
Qwen 3.5 27B	27B	A	18.3 tok/s

Frequently asked questions

Can RX 7900 XT 20GB run Gemma 4 E4B?

Yes, RX 7900 XT 20GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 80.1 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 9.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E4B?

The recommended quantization for Gemma 4 E4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E4B run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Gemma 4 E4B achieves approximately 80.1 tokens per second decode speed with a time-to-first-token of 2416ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on RX 7900 XT 20GB receives a A grade with 80.1 tok/s and 128K context.

What context window can Gemma 4 E4B use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Gemma 4 E4B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RX 7900 XT 20GB See all hardware for Gemma 4 E4B

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