Can Gemma 4 E4B run on Radeon RX 7900M 16GB?

YES — Runs Great

A81Great

○Estimated from fit model

Gemma 4 E4B needs ~9.0 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~75 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 9.0 GB, 74.9 tok/s, Runs well

9.0 GB required16.0 GB available

56% VRAM used

Fit status

Runs well

Decode

74.9 tok/s

TTFT

2586 ms

Safe context

104K

Memory

9.0 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on Radeon RX 7900M 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: 74.9 tok/s decode · 2.6s TTFT (warm) · 187 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	74.9 tok/s	1411 ms	104K
Coding	A	Runs well	74.9 tok/s	2586 ms	104K
Agentic Coding	A	Runs well	74.9 tok/s	3762 ms	104K
Reasoning	A	Runs well	74.9 tok/s	3056 ms	104K
RAG	A	Runs well	74.9 tok/s	4702 ms	104K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A74
Q3_K_S	3	3.9 GB	Low	A75
NVFP4	4	4.5 GB	Medium	A76
Q4_K_M	4	4.9 GB	Medium	A76
Q5_K_M	5	5.8 GB	High	A77
Q6_K	6	6.6 GB	High	A78
Q8_0Best for your GPU	8	8.6 GB	Very High	A79
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

ollama run gemma4:e4b

Your hardware

More models your Radeon RX 7900M 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	66.5 tok/s
👁 Alibaba Qwen 3 14B	14B	S	43 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	40.7 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	38 tok/s
👁 Mistral Ministral 3 14B	14B	S	42.8 tok/s

Frequently asked questions

See all results for Radeon RX 7900M 16GB See all hardware for Gemma 4 E4B

URL: https://willitrunai.com/can-run/gemma-4-e4b-on-rx-7900m-16gb

⇱ Gemma 4 E4B on Radeon RX 7900M 16GB? YES