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

YES — Runs Great

A74Great

○Estimated from fit model

StarCoder 15B needs ~37.0 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q5_K_M quantization, expect ~22 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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

Q5_K_M (High quality) — 37.0 GB, 21.9 tok/s, Runs well

37.0 GB required69.1 GB available

54% VRAM used

Fit status

Runs well

Decode

21.9 tok/s

TTFT

8836 ms

Safe context

Memory

37.0 GB / 69.1 GB

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsStarCoder 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: 21.9 tok/s decode · 8.8s TTFT (warm) · 55 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	A	Runs well	21.9 tok/s	4819 ms	8K
Coding	A	Runs well	21.9 tok/s	8836 ms	8K
Agentic Coding	A	Runs well	21.9 tok/s	12852 ms	8K
Reasoning	A	Runs well	21.9 tok/s	10442 ms	8K
RAG	A	Runs well	21.9 tok/s	16065 ms	8K

Quantization options

How StarCoder 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	B65
Q3_K_S	3	7.4 GB	Low	B66
NVFP4	4	8.4 GB	Medium	B66
Q4_K_M	4	9.2 GB	Medium	B66
Q5_K_M	5	10.8 GB	High	B66
Q6_K	6	12.3 GB	High	B66
Q8_0	8	16.1 GB	Very High	B67
F16Best for your GPU	16	30.7 GB	Maximum	A70

Get started

Copy-paste commands to run StarCoder 15B on your machine.

Run

lms load starcoder && lms server start

Your hardware

More models your MacBook Pro M2 Max 96GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	35.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	15.2 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	15.3 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	32.4 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	36.3 tok/s

Frequently asked questions

See all results for MacBook Pro M2 Max 96GB See all hardware for StarCoder 15B

URL: https://willitrunai.com/can-run/starcoder-15b-on-m2-max-96gb

⇱ StarCoder 15B on MacBook Pro M2 Max 96GB? YES