Can StarCoder2 15B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

C45Usable

○Estimated from fit model

StarCoder2 15B needs ~22.2 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~25 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) — 22.2 GB, 25.4 tok/s, Runs well

22.2 GB required69.1 GB available

32% VRAM used

Fit status

Runs well

Decode

25.4 tok/s

TTFT

7636 ms

Safe context

443K

Memory

22.2 GB / 69.1 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on MacBook Pro M2 Max 96GB
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: 25.4 tok/s decode · 7.6s TTFT (warm) · 63 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	25.4 tok/s	4165 ms	443K
Coding	C	Runs well	25.4 tok/s	7636 ms	443K
Agentic Coding	C	Runs well	25.4 tok/s	11106 ms	443K
Reasoning	C	Runs well	25.4 tok/s	9024 ms	443K
RAG	C	Runs well	25.4 tok/s	13883 ms	443K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C40
Q3_K_S	3	7.4 GB	Low	C40
NVFP4	4	8.4 GB	Medium	C41
Q4_K_M	4	9.2 GB	Medium	C41
Q5_K_M	5	10.8 GB	High	C41
Q6_K	6	12.3 GB	High	C41
Q8_0	8	16.1 GB	Very High	C42
F16Best for your GPU	16	30.7 GB	Maximum	C45