Can Yi Coder 9B Chat run on MacBook Pro M3 24GB?

YES — Runs Great

C48Usable

○Estimated from fit model

Yi Coder 9B Chat needs ~10.0 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 10.0 GB, 12.4 tok/s, Runs well

10.0 GB required17.3 GB available

58% VRAM used

Fit status

Runs well

Decode

12.4 tok/s

TTFT

15630 ms

Safe context

126K

Memory

10.0 GB / 17.3 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on MacBook Pro M3 24GB
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: 12.4 tok/s decode · 15.6s TTFT (warm) · 31 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	12.4 tok/s	8526 ms	126K
Coding	C	Runs well	12.4 tok/s	15630 ms	126K
Agentic Coding	C	Runs well	12.4 tok/s	22735 ms	126K
Reasoning	C	Runs well	12.4 tok/s	18472 ms	126K
RAG	C	Runs well	12.4 tok/s	28419 ms	126K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 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	5.0 GB	Medium	C48
Q4_K_M	4	5.5 GB	Medium	C49
Q5_K_M	5	6.5 GB	High	C49
Q6_K	6	7.4 GB	High	C50
Q8_0Best for your GPU	8	9.6 GB	Very High	C51
F16	16	18.5 GB	Maximum	F0