Would CUDA be a better path than Intel Arc Pro B50 16GB for Gemma 4 E4B?

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.

Can Gemma 4 E4B run on Intel Arc Pro B50 16GB?

Q: What should I upgrade first if Gemma 4 E4B feels slow on Intel Arc Pro B50 16GB?

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.

YES — Runs Great

A76Great

Estimated from fit model

Gemma 4 E4B needs ~8.7 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 8.7 GB, 20.2 tok/s, Runs well

8.7 GB required16.0 GB available

54% VRAM used

Fit status

Runs well

Decode

20.2 tok/s

TTFT

9587 ms

Safe context

108K

Memory

8.7 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on Intel Arc Pro B50 16GB
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: 20.2 tok/s decode · 9.6s TTFT (warm) · 51 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	20.2 tok/s	5229 ms	108K
Coding	A	Runs well	20.2 tok/s	9587 ms	108K
Agentic Coding	A	Runs well	20.2 tok/s	13945 ms	108K
Reasoning	A	Runs well	20.2 tok/s	11330 ms	108K
RAG	A	Runs well	20.2 tok/s	17431 ms	108K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A74
Q3_K_S	3	3.9 GB	Low	A75
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 Intel Arc Pro B50 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	23.7 tok/s
Qwen 3 14B	14B	S	15.3 tok/s

Frequently asked questions

Can Intel Arc Pro B50 16GB run Gemma 4 E4B?

Yes, Intel Arc Pro B50 16GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 20.2 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E4B?

The recommended quantization for Gemma 4 E4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E4B run at on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Gemma 4 E4B achieves approximately 20.2 tokens per second decode speed with a time-to-first-token of 9587ms using Q4_K_M quantization.

Can Intel Arc Pro B50 16GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on Intel Arc Pro B50 16GB receives a A grade with 20.2 tok/s and 108K context.

What context window can Gemma 4 E4B use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Gemma 4 E4B can safely use up to 108K 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 E4B feels slow on Intel Arc Pro B50 16GB?

See all results for Intel Arc Pro B50 16GB See all hardware for Gemma 4 E4B

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