Can Gemma 4 E4B run on Intel Arc B580 12GB?

YES — Runs Great

A81Great

○Estimated from fit model

Gemma 4 E4B needs ~8.3 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~37 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) — 8.3 GB, 36.5 tok/s, Runs well

8.3 GB required12.0 GB available

69% VRAM used

Fit status

Runs well

Decode

36.5 tok/s

TTFT

5298 ms

Safe context

63K

Memory

8.3 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on Intel Arc B580 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: 36.5 tok/s decode · 5.3s TTFT (warm) · 91 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	36.5 tok/s	2890 ms	63K
Coding	A	Runs well	36.5 tok/s	5298 ms	63K
Agentic Coding	A	Runs well	36.5 tok/s	7706 ms	63K
Reasoning	A	Runs well	36.5 tok/s	6261 ms	63K
RAG	A	Runs well	36.5 tok/s	9633 ms	63K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A77
Q3_K_S	3	3.9 GB	Low	A78
NVFP4	4	4.5 GB	Medium	A79
Q4_K_M	4	4.9 GB	Medium	A79
Q5_K_M	5	5.8 GB	High	A80
Q6_K	6	6.6 GB	High	A79
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 Intel Arc B580 12GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	42.9 tok/s
👁 Alibaba Qwen 3 14B	14B	A	17.8 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	A	14.4 tok/s
👁 Mistral Ministral 3 14B	14B	A	17.7 tok/s
👁 Microsoft Phi-4 14B	14B	B	16.1 tok/s

Frequently asked questions

See all results for Intel Arc B580 12GB See all hardware for Gemma 4 E4B

URL: https://willitrunai.com/can-run/gemma-4-e4b-on-arc-b580-12gb