Would CUDA be a better path than Intel Arc Pro B60 24GB for DeepSeek V4 Pro?

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 DeepSeek V4 Pro run on Intel Arc Pro B60 24GB?

Q: What should I upgrade first if DeepSeek V4 Pro feels slow on Intel Arc Pro B60 24GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek V4 Pro needs ~901.2 GB but Intel Arc Pro B60 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: MediumStack: StandardBottleneck: Memory capacity

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

NVFP4 (Medium quality) — 867.2 GB, exceeds 24.0 GB available

867.2 GB required24.0 GB available

3613% VRAM needed

843.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

867.2 GB / 24.0 GB

Offload

100%

Memory breakdown

Weights862.0 GB

KV Cache1.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V4 Pro 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 901.2 GB, but this setup only exposes 24.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

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	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How DeepSeek V4 Pro (1600B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	624.0 GB	Low	F0
Q3_K_S	3	784.0 GB	Low	F0
NVFP4	4

Frequently asked questions

Can Intel Arc Pro B60 24GB run DeepSeek V4 Pro?

No, DeepSeek V4 Pro requires more memory than Intel Arc Pro B60 24GB provides.

How much VRAM does DeepSeek V4 Pro need?

DeepSeek V4 Pro (1600B parameters) requires approximately 901.2 GB of memory with NVFP4 quantization.

What is the best quantization for DeepSeek V4 Pro?

The recommended quantization for DeepSeek V4 Pro is NVFP4, which balances quality and memory efficiency.

What speed will DeepSeek V4 Pro run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, DeepSeek V4 Pro achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using NVFP4 quantization.

Can Intel Arc Pro B60 24GB run DeepSeek V4 Pro for coding?

For coding workloads, DeepSeek V4 Pro on Intel Arc Pro B60 24GB receives a F grade with 2.0 tok/s and 4K context.

What context window can DeepSeek V4 Pro use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, DeepSeek V4 Pro can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek V4 Pro feels slow on Intel Arc Pro B60 24GB?

See all results for Intel Arc Pro B60 24GB See all hardware for DeepSeek V4 Pro

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