Can Gemma 4 E2B run on Intel Arc Pro B60 24GB?

YES — Runs Great

B70Good

○Estimated from fit model

Gemma 4 E2B needs ~6.9 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~60 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) — 6.9 GB, 65.2 tok/s, Runs well

6.9 GB required24.0 GB available

29% VRAM used

Fit status

Runs well

Decode

65.2 tok/s

TTFT

2968 ms

Safe context

128K

Memory

6.9 GB / 24.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on Intel Arc Pro B60 24GB
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: 65.2 tok/s decode · 3.0s TTFT (warm) · 163 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	B	Runs well	60.0 tok/s	1760 ms	128K
Coding	B	Runs well	60.0 tok/s	3227 ms	128K
Agentic Coding	A	Runs well	60.0 tok/s	4694 ms	128K
Reasoning	B	Runs well	60.0 tok/s	3814 ms	128K
RAG	A	Runs well	60.0 tok/s	5868 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	B67
Q3_K_S	3	2.5 GB	Low	B67
NVFP4	4

Get started

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

Run

ollama run gemma4:e2b

Upgrade options

Hardware that runs Gemma 4 E2B well

👁 NVIDIA
RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1336)

Raises estimated decode speed by about 49%.96.9 tok/s decode

Raises estimated decode speed by about 49%.

Adds memory headroom for longer context windows and future model growth.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value

36 GB Unified (+12)

This setup is broadly balanced for this model.71.4 tok/s decode

~$2,499 MSRP

Frequently asked questions

See all results for Intel Arc Pro B60 24GB See all hardware for Gemma 4 E2B

URL: https://willitrunai.com/can-run/gemma-4-e2b-on-arc-pro-b60-24gb

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