Would CUDA be a better path than Intel Arc Pro B50 16GB for Gemma 3 12B?

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 3 12B run on Intel Arc Pro B50 16GB?

Q: What should I upgrade first if Gemma 3 12B 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 — Tight Fit

A77Great

Estimated from fit model

Gemma 3 12B needs ~14.7 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~13 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) — 14.7 GB, 13.1 tok/s, Tight fit

14.7 GB required16.0 GB available

92% VRAM used

Fit status

Tight fit

Decode

13.1 tok/s

TTFT

14723 ms

Safe context

20K

Memory

14.7 GB / 16.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 3 12B 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: 13.1 tok/s decode · 14.7s TTFT (warm) · 33 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	13.1 tok/s	8031 ms	20K
Coding	A	Tight fit	13.1 tok/s	14723 ms	20K
Agentic Coding	F	Too heavy	6.7 tok/s	42304 ms	20K
Reasoning	A	Tight fit	13.1 tok/s	17400 ms	20K
RAG	F	Too heavy	6.7 tok/s	52880 ms	20K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A78
Q3_K_S	3	5.9 GB	Low	A79
NVFP4	4

Get started

Copy-paste commands to run Gemma 3 12B on your machine.

Run

ollama run gemma3:12b

Your hardware

More models your Intel Arc Pro B50 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3 14B	14B	S	15.3 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	14.5 tok/s

Frequently asked questions

Can Intel Arc Pro B50 16GB run Gemma 3 12B?

Yes, Intel Arc Pro B50 16GB can run Gemma 3 12B with a A grade (Tight fit). Expected decode speed: 13.1 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 14.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 12B?

The recommended quantization for Gemma 3 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 12B run at on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Gemma 3 12B achieves approximately 13.1 tokens per second decode speed with a time-to-first-token of 14723ms using Q4_K_M quantization.

Can Intel Arc Pro B50 16GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on Intel Arc Pro B50 16GB receives a A grade with 13.1 tok/s and 20K context.

What context window can Gemma 3 12B use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Gemma 3 12B can safely use up to 20K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 3 12B feels slow on Intel Arc Pro B50 16GB?

See all results for Intel Arc Pro B50 16GB See all hardware for Gemma 3 12B

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