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

YES — Runs Great

B66Good

○Estimated from fit model

Yi Coder 9B needs ~9.1 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) — 9.1 GB, 43.4 tok/s, Runs well

9.1 GB required12.0 GB available

76% VRAM used

Fit status

Runs well

Decode

43.4 tok/s

TTFT

4465 ms

Safe context

48K

Memory

9.1 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi Coder 9B 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: 43.4 tok/s decode · 4.5s TTFT (warm) · 108 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	B	Runs well	39.9 tok/s	2649 ms	48K
Coding	B	Runs well	39.9 tok/s	4856 ms	48K
Agentic Coding	B	Tight fit	39.9 tok/s	7063 ms	48K
Reasoning	B	Runs well	39.9 tok/s	5739 ms	48K
RAG	B	Tight fit	39.9 tok/s	8829 ms	48K

Quantization options

How Yi Coder 9B (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	B62
Q3_K_S	3	4.4 GB	Low	B63
NVFP4	4

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

👁 NVIDIA
RTX 5070 Ti 16GBBudget pick

16 GB VRAM (+4)896 GB/s (+440)

Raises estimated decode speed by about 162%.113.6 tok/s decode

Raises estimated decode speed by about 162%.

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

👁 NVIDIA
RTX 4070 Ti Super 16GBBest value

16 GB VRAM (+4)672 GB/s (+216)

Raises estimated decode speed by about 126%.98 tok/s decode

Raises estimated decode speed by about 126%.