Would CUDA be a better path than Intel Arc Pro B50 16GB for Yi Coder 1.5B Chat?

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 Yi Coder 1.5B Chat run on Intel Arc Pro B50 16GB?

Q: What should I upgrade first if Yi Coder 1.5B Chat 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 — Runs Great

C42Usable

Estimated from fit model

Yi Coder 1.5B Chat needs ~3.6 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 3.6 GB, 21.0 tok/s, Runs well

3.6 GB required16.0 GB available

23% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

1.1M

Memory

3.6 GB / 16.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B Chat 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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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	C	Runs well	21.0 tok/s	5029 ms	1.0M
Coding	C	Runs well	21.0 tok/s	9219 ms	1.1M
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	1.1M
Reasoning	C	Runs well	21.0 tok/s	10895 ms	1.1M
RAG	C	Runs well	21.0 tok/s	16762 ms	1.1M

Quantization options

How Yi Coder 1.5B Chat (1.5B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C45
Q3_K_S	3	0.7 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run Yi Coder 1.5B Chat on your machine.

Run

lms load hf-maziyarpanahi--yi-coder-1-5b-chat-gguf && lms server start

Upgrade options

Hardware that runs Yi Coder 1.5B Chat well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.21 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$799 MSRP

MacBook Pro M3 24GBBest value

24 GB Unified (+8)

This setup is broadly balanced for this model.21 tok/s decode

~$1,099 MSRP

Frequently asked questions

Can Intel Arc Pro B50 16GB run Yi Coder 1.5B Chat?

Yes, Intel Arc Pro B50 16GB can run Yi Coder 1.5B Chat with a C grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Yi Coder 1.5B Chat need?

Yi Coder 1.5B Chat (1.5B parameters) requires approximately 3.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 1.5B Chat?

The recommended quantization for Yi Coder 1.5B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 1.5B Chat run at on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Yi Coder 1.5B Chat achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can Intel Arc Pro B50 16GB run Yi Coder 1.5B Chat for coding?

For coding workloads, Yi Coder 1.5B Chat on Intel Arc Pro B50 16GB receives a C grade with 21.0 tok/s and 1.1M context.

What context window can Yi Coder 1.5B Chat use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, Yi Coder 1.5B Chat can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc Pro B50 16GB See all hardware for Yi Coder 1.5B Chat

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