Can StarCoder2 7B run on MacBook Pro M4 Max 128GB?

YES — Runs Great

C45Usable

○Estimated from fit model

StarCoder2 7B needs ~19.5 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~81 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) — 19.5 GB, 95.9 tok/s, Runs well

19.5 GB required92.2 GB available

21% VRAM used

Fit status

Runs well

Decode

95.9 tok/s

TTFT

2020 ms

Safe context

16K

Memory

19.5 GB / 92.2 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on MacBook Pro M4 Max 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: 95.9 tok/s decode · 2.0s TTFT (warm) · 240 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	80.6 tok/s	1311 ms	16K
Coding	C	Runs well	80.6 tok/s	2403 ms	16K
Agentic Coding	C	Runs well	80.6 tok/s	3496 ms	16K
Reasoning	C	Runs well	80.6 tok/s	2840 ms	16K
RAG	C	Runs well	80.6 tok/s	4370 ms	16K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D38
Q3_K_S	3	3.4 GB	Low	D38
NVFP4	4

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load starcoder2-7b && lms server start

Frequently asked questions

See all results for MacBook Pro M4 Max 128GB See all hardware for StarCoder2 7B

URL: https://willitrunai.com/can-run/starcoder2-7b-on-m4-max-128gb

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