Can Yi Coder 9B Chat run on Intel Arc Pro A40 6GB?

YES — With Q3_K_S

D37Poor

Estimated from fit model

Yi Coder 9B Chat needs ~7.0 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q3_K_S quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

Yi Coder 9B Chat at Q4_K_M needs 8.0 GB — too much for Intel Arc Pro A40 6GB (6.0 GB). Runs at Q3_K_S (7.0 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.0 GB, exceeds 6.0 GB available

8.0 GB required6.0 GB available

133% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.9 tok/s

TTFT

27926 ms

Safe context

Memory

8.0 GB / 6.0 GB

Offload

30%

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsYi Coder 9B Chat on Intel Arc Pro A40 6GB
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: 6.9 tok/s decode · 27.9s TTFT (warm) · 17 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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	F	Too heavy	8.0 tok/s	13206 ms	4K
Coding	F	Too heavy	6.9 tok/s	27926 ms	4K
Agentic Coding	F	Too heavy	5.3 tok/s	52645 ms	4K
Reasoning	F	Too heavy	6.9 tok/s	33003 ms	4K
RAG	F	Too heavy	5.3 tok/s	65806 ms	4K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.5 GB	Low	C54
Q3_K_S	3	4.4 GB	Low	F0
NVFP4	4	5.0 GB	Medium	F0
Q4_K_M	4	5.5 GB	Medium	F0
Q5_K_M	5	6.5 GB	High	F0
Q6_K	6	7.4 GB	High	F0
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

升级选项

能流畅运行 Yi Coder 9B Chat 的硬件

Intel Arc A580 8GB经济之选

8 GB VRAM (+2)512 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.32.2 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$179 MSRP

Intel Arc B570 10GB最佳性价比

10 GB VRAM (+4)380 GB/s (+188)

Makes the model fit on the accelerator instead of staying completely out of reach.37.4 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$219 MSRP

Intel Arc B580 12GBIntel 升级

12 GB VRAM (+6)456 GB/s (+264)

Makes the model fit on the accelerator instead of staying completely out of reach.39.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

Frequently asked questions

Can Intel Arc Pro A40 6GB run Yi Coder 9B Chat?

Yes, Intel Arc Pro A40 6GB can run Yi Coder 9B Chat at Q3_K_S quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 8.0 GB which exceeds available memory, but at Q3_K_S it needs only 7.0 GB. Expected decode speed: 10.9 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.0 GB at Q4_K_M quantization. On Intel Arc Pro A40 6GB, it fits at Q3_K_S using 7.0 GB.

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

The recommended quantization is Q4_K_M, but on Intel Arc Pro A40 6GB the best fitting quantization is Q3_K_S, which uses 7.0 GB.

What speed will Yi Coder 9B Chat run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, Yi Coder 9B Chat achieves approximately 10.9 tokens per second decode speed with a time-to-first-token of 17807ms using Q3_K_S quantization.

Can Intel Arc Pro A40 6GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on Intel Arc Pro A40 6GB receives a F grade with 6.9 tok/s and 4K context.

What context window can Yi Coder 9B Chat use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, Yi Coder 9B Chat can safely use up to 4K tokens of context at Q3_K_S quantization. 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 Pro A40 6GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Would CUDA be a better path than Intel Arc Pro A40 6GB 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.

See all results for Intel Arc Pro A40 6GB See all hardware for Yi Coder 9B Chat

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-9b-chat-gguf-on-arc-pro-a40-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: