Can Yi Coder 1.5B run on MacBook Pro M4 Pro 64GB?

YES — Runs Great

C41Usable

▲Estimated — low-sample bucket· few comparable runs

Yi Coder 1.5B needs ~8.9 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~21 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) — 8.9 GB, 21.0 tok/s, Runs well

8.9 GB required46.1 GB available

19% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

3.4M

Memory

8.9 GB / 46.1 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B on MacBook Pro M4 Pro 64GB
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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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	21.0 tok/s	5029 ms	3.0M
Coding	C	Runs well	21.0 tok/s	9219 ms	3.4M
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	3.4M
Reasoning	C	Runs well	21.0 tok/s	10895 ms	3.4M
RAG	C	Runs well	21.0 tok/s	16762 ms	3.4M

Quantization options

How Yi Coder 1.5B (1.5B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C41
Q3_K_S	3	0.7 GB	Low	C41
NVFP4	4	0.8 GB	Medium	C41
Q4_K_M	4	0.9 GB	Medium	C41
Q5_K_M	5	1.1 GB	High	C41
Q6_K	6	1.2 GB	High	C41
Q8_0	8	1.6 GB	Very High	C41
F16Best for your GPU	16	3.1 GB	Maximum	C41

Get started

Copy-paste commands to run Yi Coder 1.5B on your machine.

Run

lms load hf-lmstudio-community--yi-coder-1-5b-gguf && lms server start

Frequently asked questions

See all results for MacBook Pro M4 Pro 64GB See all hardware for Yi Coder 1.5B

URL: https://willitrunai.com/can-run/hf-lmstudio-community--yi-coder-1-5b-gguf-on-m4-pro-64gb

⇱ Yi Coder 1.5B on MacBook Pro M4 Pro 64GB? YES