Can Gemma 4 E2B run on RX 9070 16GB?

YES — Runs Great

A72Great

○Estimated from fit model

Gemma 4 E2B needs ~6.1 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~71 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 6.1 GB, 71.4 tok/s, Runs well

6.1 GB required16.0 GB available

38% VRAM used

Fit status

Runs well

Decode

71.4 tok/s

TTFT

2711 ms

Safe context

128K

Memory

6.1 GB / 16.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on RX 9070 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: 71.4 tok/s decode · 2.7s TTFT (warm) · 179 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	71.4 tok/s	1479 ms	128K
Coding	A	Runs well	71.4 tok/s	2711 ms	128K
Agentic Coding	A	Runs well	71.4 tok/s	3944 ms	128K
Reasoning	A	Runs well	71.4 tok/s	3204 ms	128K
RAG	A	Runs well	71.4 tok/s	4930 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	B69
Q3_K_S	3	2.5 GB	Low	B69
NVFP4	4	2.9 GB	Medium	B70
Q4_K_M	4	3.1 GB	Medium	B70
Q5_K_M	5	3.7 GB	High	A70
Q6_K	6	4.2 GB	High	A71
Q8_0	8	5.5 GB	Very High	A72
F16Best for your GPU	16	10.5 GB	Maximum	A74

Get started

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

Run

ollama run gemma4:e2b

Your hardware

More models your RX 9070 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	77.7 tok/s
👁 Alibaba Qwen 3 14B	14B	S	50.2 tok/s
👁 Alibaba Qwen 3 8B	8B	S	87.4 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	47.6 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	47.1 tok/s

Frequently asked questions

See all results for RX 9070 16GB See all hardware for Gemma 4 E2B

URL: https://willitrunai.com/can-run/gemma-4-e2b-on-rx-9070-16gb