Can Gemma 4 E2B run on Intel Arc Pro A40 6GB?

YES — Tight Fit

A72Great

○Estimated from fit model

Gemma 4 E2B needs ~5.1 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 5.1 GB, 24.9 tok/s, Tight fit

5.1 GB required6.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

24.9 tok/s

TTFT

7768 ms

Safe context

42K

Memory

5.1 GB / 6.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on Intel Arc Pro A40 6GB
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: 24.9 tok/s decode · 7.8s TTFT (warm) · 62 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	22.9 tok/s	4608 ms	42K
Coding	A	Tight fit	22.9 tok/s	8447 ms	42K
Agentic Coding	A	Tight fit	22.9 tok/s	12287 ms	42K
Reasoning	A	Tight fit	22.9 tok/s	9983 ms	42K
RAG	A	Tight fit	22.9 tok/s	15359 ms	42K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	A77
Q3_K_S	3	2.5 GB	Low	A77
NVFP4	4

Get started

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

Run

ollama run gemma4:e2b

Your hardware

More models your Intel Arc Pro A40 6GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 2.5 VL 7B	7B	B	14.6 tok/s
👁 Alibaba Qwen 2.5 7B	7B	B	14.6 tok/s

Frequently asked questions

See all results for Intel Arc Pro A40 6GB See all hardware for Gemma 4 E2B

URL: https://willitrunai.com/can-run/gemma-4-e2b-on-arc-pro-a40-6gb

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