Can starcoder2 15b instruct v0.1 run on MacBook Pro M4 Pro 24GB?

YES — Tight Fit

C49Usable

▲Estimated — low-sample bucket· few comparable runs

starcoder2 15b instruct v0.1 needs ~14.4 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 14.4 GB, 21.2 tok/s, Tight fit

14.4 GB required17.3 GB available

83% VRAM used

Fit status

Tight fit

Decode

21.2 tok/s

TTFT

9120 ms

Safe context

42K

Memory

14.4 GB / 17.3 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on MacBook Pro M4 Pro 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: 21.2 tok/s decode · 9.1s 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	21.2 tok/s	4975 ms	42K
Coding	C	Tight fit	21.2 tok/s	9120 ms	42K
Agentic Coding	C	Tight fit	21.2 tok/s	13266 ms	42K
Reasoning	C	Tight fit	21.2 tok/s	10778 ms	42K
RAG	C	Tight fit	21.2 tok/s	16582 ms	42K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C49
Q3_K_S	3	7.4 GB	Low	C50
NVFP4	4	8.4 GB	Medium	C51
Q4_K_M	4	9.2 GB	Medium	C51
Q5_K_M	5	10.8 GB	High	C50
Q6_KBest for your GPU	6	12.3 GB	High	C50
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0