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 ~65 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	65.2 tok/s	1619 ms	128K
Coding	B	Runs well	65.2 tok/s	2968 ms	128K
Agentic Coding	A	Runs well	65.2 tok/s	4317 ms	128K
Reasoning	B	Runs well	65.2 tok/s	3507 ms	128K
RAG	A	Runs well	65.2 tok/s	5396 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	2.9 GB	Medium	B67
Q4_K_M	4	3.1 GB	Medium	B67
Q5_K_M	5	3.7 GB	High	B68
Q6_K	6	4.2 GB	High	B68
Q8_0	8	5.5 GB	Very High	B69
F16Best for your GPU	16	10.5 GB	Maximum	A72

Get started

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

Run

ollama run gemma4:e2b

升级选项

能流畅运行 Gemma 4 E2B 的硬件

RTX 5090 32GB经济之选

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

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

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 36GB最佳性价比

36 GB Unified (+12)

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

~$2,499 MSRP

Frequently asked questions

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

Yes, Intel Arc Pro B60 24GB can run Gemma 4 E2B with a B grade (Runs well). Expected decode speed: 65.2 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 6.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E2B?

The recommended quantization for Gemma 4 E2B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E2B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Gemma 4 E2B achieves approximately 65.2 tokens per second decode speed with a time-to-first-token of 2968ms using Q4_K_M quantization.

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

For coding workloads, Gemma 4 E2B on Intel Arc Pro B60 24GB receives a B grade with 65.2 tok/s and 128K context.

What context window can Gemma 4 E2B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Gemma 4 E2B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 E2B feels slow on Intel Arc Pro B60 24GB?

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.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Gemma 4 E2B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

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

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