Can Yi 9B Coder i1 run on MacBook Pro M4 Max 36GB?

YES — Runs Great

C49Usable

○Estimated from fit model

Yi 9B Coder i1 needs ~11.3 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 11.3 GB, 51.3 tok/s, Runs well

11.3 GB required25.9 GB available

44% VRAM used

Fit status

Runs well

Decode

51.3 tok/s

TTFT

3775 ms

Safe context

237K

Memory

11.3 GB / 25.9 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on MacBook Pro M4 Max 36GB
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: 51.3 tok/s decode · 3.8s TTFT (warm) · 128 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	51.3 tok/s	2059 ms	237K
Coding	C	Runs well	51.3 tok/s	3775 ms	237K
Agentic Coding	C	Runs well	51.3 tok/s	5491 ms	237K
Reasoning	C	Runs well	51.3 tok/s	4461 ms	237K
RAG	C	Runs well	51.3 tok/s	6864 ms	237K

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C44
Q3_K_S	3	4.4 GB	Low	C44
NVFP4	4	5.0 GB	Medium	C45
Q4_K_M	4	5.5 GB	Medium	C45
Q5_K_M	5	6.5 GB	High	C45
Q6_K	6	7.4 GB	High	C46
Q8_0	8	9.6 GB	Very High	C47
F16Best for your GPU	16	18.5 GB	Maximum	C49

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 MacBook Pro M4 Max 36GB See all hardware for Yi 9B Coder i1

URL: https://willitrunai.com/can-run/hf-mradermacher--yi-9b-coder-i1-gguf-on-m4-max-36gb

⇱ Yi 9B Coder i1 on MacBook Pro M4 Max 36GB? YES