Can Gemma 4 E4B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

A76Great

○Estimated from fit model

Gemma 4 E4B needs ~10.6 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~77 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) — 10.6 GB, 83.2 tok/s, Runs well

10.6 GB required32.0 GB available

33% VRAM used

Fit status

Runs well

Decode

83.2 tok/s

TTFT

2327 ms

Safe context

128K

Memory

10.6 GB / 32.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on Radeon AI PRO R9700 32GB
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: 83.2 tok/s decode · 2.3s TTFT (warm) · 208 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	77.4 tok/s	1365 ms	128K
Coding	A	Runs well	77.4 tok/s	2502 ms	128K
Agentic Coding	A	Runs well	77.4 tok/s	3639 ms	128K
Reasoning	A	Runs well	77.4 tok/s	2957 ms	128K
RAG	A	Runs well	77.4 tok/s	4549 ms	128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A70
Q3_K_S	3	3.9 GB	Low	A71
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 Radeon AI PRO R9700 32GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	57.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	24.8 tok/s

Frequently asked questions

See all results for Radeon AI PRO R9700 32GB See all hardware for Gemma 4 E4B

URL: https://willitrunai.com/can-run/gemma-4-e4b-on-radeon-ai-pro-r9700-32gb

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