Can Yi 9B Coder i1 run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

C46Usable

○Estimated from fit model

Yi 9B Coder i1 needs ~21.3 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~85 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 21.3 GB, 84.5 tok/s, Runs well

21.3 GB required92.2 GB available

23% VRAM used

Fit status

Runs well

Decode

84.5 tok/s

TTFT

2291 ms

Safe context

1.1M

Memory

21.3 GB / 92.2 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on Mac Studio M2 Ultra 128GB
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: 84.5 tok/s decode · 2.3s TTFT (warm) · 211 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	84.5 tok/s	1249 ms	1.1M
Coding	C	Runs well	84.5 tok/s	2291 ms	1.1M
Agentic Coding	C	Runs well	84.5 tok/s	3332 ms	1.1M
Reasoning	C	Runs well	84.5 tok/s	2707 ms	1.1M
RAG	C	Runs well	84.5 tok/s	4165 ms	1.1M

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	D39
Q3_K_S	3	4.4 GB	Low	D39
NVFP4	4	5.0 GB	Medium	D39
Q4_K_M	4	5.5 GB	Medium	D39
Q5_K_M	5	6.5 GB	High	D39
Q6_K	6	7.4 GB	High	D39
Q8_0	8	9.6 GB	Very High	D39
F16Best for your GPU	16	18.5 GB	Maximum	C40

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

Frequently asked questions

See all results for Mac Studio M2 Ultra 128GB See all hardware for Yi 9B Coder i1

URL: https://willitrunai.com/can-run/hf-mradermacher--yi-9b-coder-i1-gguf-on-m2-ultra-128gb

⇱ Yi 9B Coder i1 on Mac Studio M2 Ultra 128GB? YES