Can Yi 9B Coder i1 run on Intel Arc A770 16GB?

YES — Runs Great

C51Usable

○Estimated from fit model

Yi 9B Coder i1 needs ~9.0 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~46 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) — 9.0 GB, 45.9 tok/s, Runs well

9.0 GB required16.0 GB available

56% VRAM used

Fit status

Runs well

Decode

45.9 tok/s

TTFT

4218 ms

Safe context

122K

Memory

9.0 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on Intel Arc A770 16GB
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: 45.9 tok/s decode · 4.2s TTFT (warm) · 115 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	45.9 tok/s	2301 ms	122K
Coding	C	Runs well	45.9 tok/s	4218 ms	122K
Agentic Coding	C	Runs well	45.9 tok/s	6135 ms	122K
Reasoning	C	Runs well	45.9 tok/s	4985 ms	122K
RAG	C	Runs well	45.9 tok/s	7669 ms	122K

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C47
Q3_K_S	3	4.4 GB	Low	C48
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

Upgrade options

Hardware that runs Yi 9B Coder i1 well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+240)

Raises estimated decode speed by about 90%.87.4 tok/s decode

Raises estimated decode speed by about 90%.

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

~$899 MSRP

👁 NVIDIA
RTX A4500 20GBBest value

20 GB VRAM (+4)640 GB/s (+80)

Raises estimated decode speed by about 98%.90.9 tok/s decode

Raises estimated decode speed by about 98%.