Can Gemma 4 E2B run on Radeon RX 7800M 12GB?

YES — Runs Great

A74Great

Estimated from fit model

Gemma 4 E2B needs ~5.7 GB VRAM. Radeon RX 7800M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~68 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 5.7 GB, 67.5 tok/s, Runs well

5.7 GB required12.0 GB available

48% VRAM used

Fit status

Runs well

Decode

67.5 tok/s

TTFT

2867 ms

Safe context

128K

Memory

5.7 GB / 12.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on Radeon RX 7800M 12GB
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: 67.5 tok/s decode · 2.9s TTFT (warm) · 169 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	67.5 tok/s	1564 ms	128K
Coding	A	Runs well	67.5 tok/s	2867 ms	128K
Agentic Coding	A	Runs well	67.5 tok/s	4170 ms	128K
Reasoning	A	Runs well	67.5 tok/s	3388 ms	128K
RAG	A	Runs well	67.5 tok/s	5213 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on Radeon RX 7800M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	A71
Q3_K_S	3	2.5 GB	Low	A72
NVFP4	4	2.9 GB	Medium	A72
Q4_K_M	4	3.1 GB	Medium	A72
Q5_K_M	5	3.7 GB	High	A73
Q6_K	6	4.2 GB	High	A74
Q8_0Best for your GPU	8	5.5 GB	Very High	A75
F16	16	10.5 GB	Maximum	F0

Get started

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

Run

ollama run gemma4:e2b

Your hardware

More models your Radeon RX 7800M 12GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	49.9 tok/s
Qwen 3 14B	14B	A	20.2 tok/s
Qwen 3 8B	8B	S	56.1 tok/s
Phi-4-reasoning-plus 14B	14.7B	A	16.3 tok/s
Nemotron Nano 8B	8B	S	56.1 tok/s

Frequently asked questions

Can Radeon RX 7800M 12GB run Gemma 4 E2B?

Yes, Radeon RX 7800M 12GB can run Gemma 4 E2B with a A grade (Runs well). Expected decode speed: 67.5 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 5.7 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 RX 7800M 12GB?

On Radeon RX 7800M 12GB, Gemma 4 E2B achieves approximately 67.5 tokens per second decode speed with a time-to-first-token of 2867ms using Q4_K_M quantization.

Can Radeon RX 7800M 12GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on Radeon RX 7800M 12GB receives a A grade with 67.5 tok/s and 128K context.

What context window can Gemma 4 E2B use on Radeon RX 7800M 12GB?

On Radeon RX 7800M 12GB, Gemma 4 E2B 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 Radeon RX 7800M 12GB See all hardware for Gemma 4 E2B

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<iframe src="https://willitrunai.com/embed/gemma-4-e2b-on-rx-7800m-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

67.5 tok/s

1564 ms

128K

Coding

Runs well

67.5 tok/s

2867 ms

128K

Agentic Coding

Runs well

67.5 tok/s

4170 ms

128K

Reasoning

Runs well

67.5 tok/s

3388 ms

128K

RAG

Runs well

67.5 tok/s

5213 ms

128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on Radeon RX 7800M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	A71
Q3_K_S	3	2.5 GB	Low	A72
NVFP4	4	2.9 GB	Medium	A72
Q4_K_M	4	3.1 GB	Medium	A72
Q5_K_M	5	3.7 GB	High	A73
Q6_K	6	4.2 GB	High	A74
Q8_0Best for your GPU	8	5.5 GB	Very High	A75
F16	16	10.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

49.9 tok/s

Qwen 3 14B

14B

20.2 tok/s

Qwen 3 8B

56.1 tok/s

Phi-4-reasoning-plus 14B

14.7B

16.3 tok/s

Nemotron Nano 8B

56.1 tok/s

Frequently asked questions

Can Radeon RX 7800M 12GB run Gemma 4 E2B?

Yes, Radeon RX 7800M 12GB can run Gemma 4 E2B with a A grade (Runs well). Expected decode speed: 67.5 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 5.7 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 RX 7800M 12GB?

On Radeon RX 7800M 12GB, Gemma 4 E2B achieves approximately 67.5 tokens per second decode speed with a time-to-first-token of 2867ms using Q4_K_M quantization.

Can Radeon RX 7800M 12GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on Radeon RX 7800M 12GB receives a A grade with 67.5 tok/s and 128K context.

What context window can Gemma 4 E2B use on Radeon RX 7800M 12GB?

On Radeon RX 7800M 12GB, Gemma 4 E2B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.