Can StarCoder2 15B run on Mac mini M4 32GB?

YES — Runs Great

C49Usable

▲Estimated — low-sample bucket· few comparable runs

StarCoder2 15B needs ~15.3 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 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) — 15.3 GB, 8.7 tok/s, Runs well

15.3 GB required23.0 GB available

67% VRAM used

Fit status

Runs well

Decode

8.7 tok/s

TTFT

22189 ms

Safe context

87K

Memory

15.3 GB / 23.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on Mac mini M4 32GB
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: 8.7 tok/s decode · 22.2s TTFT (warm) · 22 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	8.7 tok/s	12103 ms	87K
Coding	C	Runs well	8.7 tok/s	22189 ms	87K
Agentic Coding	C	Runs well	8.7 tok/s	32275 ms	87K
Reasoning	C	Runs well	8.7 tok/s	26224 ms	87K
RAG	C	Runs well	8.7 tok/s	40344 ms	87K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C46
Q3_K_S	3	7.4 GB	Low	C47
NVFP4	4	8.4 GB	Medium	C48
Q4_K_M	4	9.2 GB	Medium	C48
Q5_K_M	5	10.8 GB	High	C50
Q6_K	6	12.3 GB	High	C50
Q8_0Best for your GPU	8	16.1 GB	Very High	C50
F16	16	30.7 GB	Maximum	F0