Would CUDA be a better path than Intel Arc B580 12GB for Yi 9B Coder i1?

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 9B Coder i1 run on Intel Arc B580 12GB?

Q: What should I upgrade first if Yi 9B Coder i1 feels slow on Intel Arc B580 12GB?

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

C54Usable

Estimated from fit model

Yi 9B Coder i1 needs ~8.6 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 8.6 GB, 39.9 tok/s, Runs well

8.6 GB required12.0 GB available

72% VRAM used

Fit status

Runs well

Decode

39.9 tok/s

TTFT

4856 ms

Safe context

67K

Memory

8.6 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on Intel Arc B580 12GB
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: 39.9 tok/s decode · 4.9s 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.

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	39.9 tok/s	2649 ms	67K
Coding	C	Runs well	39.9 tok/s	4856 ms	67K
Agentic Coding	C	Runs well	39.9 tok/s	7063 ms	67K
Reasoning	C	Runs well	39.9 tok/s	5739 ms	67K
RAG	C	Runs well	39.9 tok/s	8829 ms	67K

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run Yi 9B Coder i1 on your machine.

Run

lms load hf-mradermacher--yi-9b-coder-i1-gguf && lms server start

Frequently asked questions

Can Intel Arc B580 12GB run Yi 9B Coder i1?

Yes, Intel Arc B580 12GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 39.9 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 9B Coder i1?

The recommended quantization for Yi 9B Coder i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 9B Coder i1 run at on Intel Arc B580 12GB?

On Intel Arc B580 12GB, Yi 9B Coder i1 achieves approximately 39.9 tokens per second decode speed with a time-to-first-token of 4856ms using Q4_K_M quantization.

Can Intel Arc B580 12GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on Intel Arc B580 12GB receives a C grade with 39.9 tok/s and 67K context.

What context window can Yi 9B Coder i1 use on Intel Arc B580 12GB?

On Intel Arc B580 12GB, Yi 9B Coder i1 can safely use up to 67K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Yi 9B Coder i1 feels slow on Intel Arc B580 12GB?

See all results for Intel Arc B580 12GB See all hardware for Yi 9B Coder i1

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