Would CUDA be a better path than Intel Arc Pro B60 24GB for Gemma 4 26B A4B?

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 26B A4B run on Intel Arc Pro B60 24GB?

Q: What should I upgrade first if Gemma 4 26B A4B 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.

YES — Tight Fit

S86Excellent

Estimated from fit model

Gemma 4 26B A4B needs ~22.3 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 22.3 GB, 40.0 tok/s, Tight fit

22.3 GB required24.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

40.0 tok/s

TTFT

4842 ms

Safe context

23K

Memory

22.3 GB / 24.0 GB

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 4 26B A4B 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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	40.0 tok/s	2641 ms	23K
Coding	S	Tight fit	40.0 tok/s	4842 ms	23K
Agentic Coding	A	Very compromised (needs ~1.2 GB host RAM)	26.0 tok/s	10840 ms	23K
Reasoning	S	Tight fit	40.0 tok/s	5722 ms	23K
RAG	A	Very compromised (needs ~1.2 GB host RAM)	26.0 tok/s	13549 ms

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	A84
Q3_K_S	3	12.3 GB	Low	S85
NVFP4	4

Get started

Copy-paste commands to run Gemma 4 26B A4B on your machine.

Run

ollama run gemma4:26b

Your hardware

More models your Intel Arc Pro B60 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	37.2 tok/s
Qwen 3.5 27B	27B	S	16.1 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run Gemma 4 26B A4B?

Yes, Intel Arc Pro B60 24GB can run Gemma 4 26B A4B with a S grade (Tight fit). Expected decode speed: 40.0 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 22.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 26B A4B?

The recommended quantization for Gemma 4 26B A4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 26B A4B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Gemma 4 26B A4B achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4842ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on Intel Arc Pro B60 24GB receives a S grade with 40.0 tok/s and 23K context.

What context window can Gemma 4 26B A4B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Gemma 4 26B A4B can safely use up to 23K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 26B A4B feels slow on Intel Arc Pro B60 24GB?

See all results for Intel Arc Pro B60 24GB See all hardware for Gemma 4 26B A4B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/gemma-4-26b-a4b-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: