Would CUDA be a better path than Intel Arc Pro B50 16GB for CodeLlama 7B Instruct?

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 CodeLlama 7B Instruct run on Intel Arc Pro B50 16GB?

Q: What should I upgrade first if CodeLlama 7B Instruct 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

A73Great

Estimated from fit model

CodeLlama 7B Instruct needs ~14.6 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~28 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) — 14.6 GB, 28.3 tok/s, Tight fit

14.6 GB required16.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

28.3 tok/s

TTFT

6834 ms

Safe context

16K

Memory

14.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodeLlama 7B Instruct 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: 28.3 tok/s decode · 6.8s TTFT (warm) · 71 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	28.3 tok/s	3728 ms	16K
Coding	A	Tight fit	28.3 tok/s	6834 ms	16K
Agentic Coding	F	Too heavy	10.9 tok/s	25812 ms	16K
Reasoning	A	Tight fit	28.3 tok/s	8077 ms	16K
RAG	F	Too heavy	10.9 tok/s	32265 ms	16K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_S	3	3.4 GB	Low	A71
NVFP4	4

Get started

Copy-paste commands to run CodeLlama 7B Instruct on your machine.

Run

lms load CodeLlama-7b-Instruct-hf && lms server start

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 CodeLlama 7B Instruct?

Yes, Intel Arc Pro B50 16GB can run CodeLlama 7B Instruct with a A grade (Tight fit). Expected decode speed: 28.3 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 14.6 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 7B Instruct?

The recommended quantization for CodeLlama 7B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 7B Instruct run at on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, CodeLlama 7B Instruct achieves approximately 28.3 tokens per second decode speed with a time-to-first-token of 6834ms using Q4_K_M quantization.

Can Intel Arc Pro B50 16GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on Intel Arc Pro B50 16GB receives a A grade with 28.3 tok/s and 16K context.

What context window can CodeLlama 7B Instruct use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, CodeLlama 7B Instruct can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for Intel Arc Pro B50 16GB See all hardware for CodeLlama 7B Instruct

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