Can Yi Coder 9B run on Intel Arc A380 6GB?

YES — With Q2_K

C50Usable

○Estimated from fit model

Yi Coder 9B needs ~6.5 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q2_K quantization, expect ~15 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 at Q4_K_M needs 8.5 GB — too much for Intel Arc A380 6GB (6.0 GB). Runs at Q2_K (6.5 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.5 GB, exceeds 6.0 GB available

8.5 GB required6.0 GB available

142% VRAM needed

2.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.6 tok/s

TTFT

29432 ms

Safe context

Memory

8.5 GB / 6.0 GB

Offload

30%

Memory breakdown

Weights5.5 GB

KV Cache1.5 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 on Intel Arc A380 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: 6.6 tok/s decode · 29.4s TTFT (warm) · 16 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	13266 ms	4K
Coding	F	Too heavy	6.6 tok/s	29432 ms	4K
Agentic Coding	F	Too heavy	4.7 tok/s	59927 ms	4K
Reasoning	F	Too heavy	6.6 tok/s	34784 ms	4K
RAG	F	Too heavy	4.7 tok/s	74909 ms	4K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.5 GB	Low	B66
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 on your machine.

Run

lms load Yi-Coder-9B-Chat && lms server start

Upgrade options

Hardware that runs Yi Coder 9B well

👁 Intel
Intel Arc A580 8GBBest value

8 GB VRAM (+2)512 GB/s (+326)

Makes the model fit on the accelerator instead of staying completely out of reach.31.6 tok/s decode

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

Raises estimated decode speed by about 379%.

~$179 MSRP

👁 Intel
Intel Arc B570 10GBBudget pick

10 GB VRAM (+4)380 GB/s (+194)

Makes the model fit on the accelerator instead of staying completely out of reach.40.6 tok/s decode

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
Intel Arc B580 12GBIntel upgrade

12 GB VRAM (+6)456 GB/s (+270)

Makes the model fit on the accelerator instead of staying completely out of reach.43.4 tok/s decode

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

See all results for Intel Arc A380 6GB See all hardware for Yi Coder 9B

URL: https://willitrunai.com/can-run/yi-coder-9b-on-arc-a380-6gb

⇱ Yi Coder 9B on Intel Arc A380 6GB? No — Alternatives

Can Yi Coder 9B run on Intel Arc A380 6GB?

YES — With Q2_K

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Yi Coder 9B well

Frequently asked questions