Can Yi Coder 9B Chat run on Intel Arc A730M 12GB?

YES — Runs Great

C53Usable

Estimated from fit model

Yi Coder 9B Chat needs ~8.6 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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, 30.0 tok/s, Runs well

8.6 GB required12.0 GB available

72% VRAM used

Fit status

Runs well

Decode

30.0 tok/s

TTFT

6456 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 Coder 9B Chat on Intel Arc A730M 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: 30.0 tok/s decode · 6.5s TTFT (warm) · 75 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	30.0 tok/s	3521 ms	67K
Coding	C	Runs well	30.0 tok/s	6456 ms	67K
Agentic Coding	C	Runs well	30.0 tok/s	9390 ms	67K
Reasoning	C	Runs well	30.0 tok/s	7629 ms	67K
RAG	C	Runs well	30.0 tok/s	11738 ms	67K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on Intel Arc A730M 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	5.0 GB	Medium	C52
Q4_K_M	4	5.5 GB	Medium	C53
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Get started

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

Run

lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server start

Opções de upgrade

Hardware que roda bem Yi Coder 9B Chat

RX 9070 XT 16GBOpção econômica

16 GB VRAM (+4)640 GB/s (+304)

Raises estimated decode speed by about 149%.74.6 tok/s decodificação

Raises estimated decode speed by about 149%.

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

~$549 MSRP

RTX 5070 Ti 16GBMelhor custo-benefício

16 GB VRAM (+4)896 GB/s (+560)

Raises estimated decode speed by about 248%.104.5 tok/s decodificação

Raises estimated decode speed by about 248%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$749 MSRP

Frequently asked questions

Can Intel Arc A730M 12GB run Yi Coder 9B Chat?

Yes, Intel Arc A730M 12GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 30.0 tok/s.

How much VRAM does Yi Coder 9B Chat need?

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

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

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

What speed will Yi Coder 9B Chat run at on Intel Arc A730M 12GB?

On Intel Arc A730M 12GB, Yi Coder 9B Chat achieves approximately 30.0 tokens per second decode speed with a time-to-first-token of 6456ms using Q4_K_M quantization.

Can Intel Arc A730M 12GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on Intel Arc A730M 12GB receives a C grade with 30.0 tok/s and 67K context.

What context window can Yi Coder 9B Chat use on Intel Arc A730M 12GB?

On Intel Arc A730M 12GB, Yi Coder 9B Chat 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 Coder 9B Chat feels slow on Intel Arc A730M 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.

Would CUDA be a better path than Intel Arc A730M 12GB for Yi Coder 9B 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.

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