Can Gemma 4 E2B run on Radeon Pro W7500 8GB?

YES — Runs Great

A77Great

Estimated from fit model

Gemma 4 E2B needs ~5.6 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 5.6 GB, 46.2 tok/s, Runs well

5.6 GB required8.0 GB available

70% VRAM used

Fit status

Runs well

Decode

46.2 tok/s

TTFT

4191 ms

Safe context

87K

Memory

5.6 GB / 8.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on Radeon Pro W7500 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: 46.2 tok/s decode · 4.2s TTFT (warm) · 116 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	42.5 tok/s	2486 ms	87K
Coding	A	Runs well	42.5 tok/s	4557 ms	87K
Agentic Coding	A	Runs well	42.5 tok/s	6629 ms	87K
Reasoning	A	Runs well	42.5 tok/s	5386 ms	87K
RAG	A	Runs well	42.5 tok/s	8286 ms	87K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	A75
Q3_K_S	3	2.5 GB	Low	A76
NVFP4	4

Get started

Copy-paste commands to run Gemma 4 E2B on your machine.

Run

ollama run gemma4:e2b

Your hardware

More models your Radeon Pro W7500 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3 8B	8B	A	16.7 tok/s
Nemotron Nano 8B	8B	A	17.7 tok/s

Frequently asked questions

Can Radeon Pro W7500 8GB run Gemma 4 E2B?

Yes, Radeon Pro W7500 8GB can run Gemma 4 E2B with a A grade (Runs well). Expected decode speed: 42.5 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 5.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E2B?

The recommended quantization for Gemma 4 E2B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E2B run at on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, Gemma 4 E2B achieves approximately 42.5 tokens per second decode speed with a time-to-first-token of 4557ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on Radeon Pro W7500 8GB receives a A grade with 42.5 tok/s and 87K context.

What context window can Gemma 4 E2B use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, Gemma 4 E2B can safely use up to 87K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for Radeon Pro W7500 8GB See all hardware for Gemma 4 E2B

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