Would CUDA be a better path than Intel Arc Pro B50 16GB for DeepSeek Coder V2 16B?

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 Coder V2 16B run on Intel Arc Pro B50 16GB?

Q: What should I upgrade first if DeepSeek Coder V2 16B 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 — With Offload

A79Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~15.6 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 15.6 GB, 29.5 tok/s, Runs with offload

15.6 GB required16.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

29.5 tok/s

TTFT

6561 ms

Safe context

18K

Memory

15.6 GB / 16.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B 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: 29.5 tok/s decode · 6.6s TTFT (warm) · 74 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	A	Tight fit	29.5 tok/s	3579 ms	18K
Coding	A	Runs with offload	29.5 tok/s	6561 ms	18K
Agentic Coding	B	Very compromised (needs ~1.5 GB host RAM)	16.1 tok/s	17439 ms	18K
Reasoning	A	Runs with offload	29.5 tok/s	7754 ms	18K
RAG	B	Very compromised (needs ~1.5 GB host RAM)	16.1 tok/s	21798 ms

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A79
Q3_K_S	3	7.8 GB	Low	A80
NVFP4	4

Get started

Copy-paste commands to run DeepSeek Coder V2 16B on your machine.

Run

lms load DeepSeek-Coder-V2-Lite-Instruct && lms server start

Your hardware

More models your Intel Arc Pro B50 16GB can run

Model	Params	Grade	Decode	Capabilities
GPT-OSS 20B	21B	A	14.4 tok/s
Codestral 2 25.08	22B	B

Frequently asked questions

Can Intel Arc Pro B50 16GB run DeepSeek Coder V2 16B?

Yes, Intel Arc Pro B50 16GB can run DeepSeek Coder V2 16B with a A grade (Runs with offload). Expected decode speed: 29.5 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 16B?

The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 16B run at on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, DeepSeek Coder V2 16B achieves approximately 29.5 tokens per second decode speed with a time-to-first-token of 6561ms using Q4_K_M quantization.

Can Intel Arc Pro B50 16GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on Intel Arc Pro B50 16GB receives a A grade with 29.5 tok/s and 18K context.

What context window can DeepSeek Coder V2 16B use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, DeepSeek Coder V2 16B can safely use up to 18K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Intel Arc Pro B50 16GB See all hardware for DeepSeek Coder V2 16B

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