Can StarCoder 7B run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

A77Great

○Estimated from fit model

StarCoder 7B needs ~16.0 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~33 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) — 16.0 GB, 32.8 tok/s, Runs well

16.0 GB required23.0 GB available

70% VRAM used

Fit status

Runs well

Decode

32.8 tok/s

TTFT

5905 ms

Safe context

Memory

16.0 GB / 23.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsStarCoder 7B on MacBook Pro M2 Pro 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: 32.8 tok/s decode · 5.9s TTFT (warm) · 82 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	32.8 tok/s	3221 ms	8K
Coding	A	Runs well	32.8 tok/s	5905 ms	8K
Agentic Coding	A	Runs with offload (needs ~0 GB host RAM)	32.0 tok/s	8811 ms	8K
Reasoning	A	Runs well	32.8 tok/s	6978 ms	8K
RAG	A	Runs with offload (needs ~0 GB host RAM)	32.0 tok/s	11014 ms	8K

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B68
Q3_K_S	3	3.4 GB	Low	B68
NVFP4	4	3.9 GB	Medium	B68
Q4_K_M	4	4.3 GB	Medium	B69
Q5_K_M	5	5.0 GB	High	B69
Q6_K	6	5.7 GB	High	B69
Q8_0	8	7.5 GB	Very High	A71
F16Best for your GPU	16	14.3 GB	Maximum	A73

Get started

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

Run

lms load starcoder-7b && lms server start

Your hardware

More models your MacBook Pro M2 Pro 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	A	19 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	8.5 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	7 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	20.1 tok/s
👁 Alibaba Qwen 3.5 9B	9B	S	27.4 tok/s

Frequently asked questions

See all results for MacBook Pro M2 Pro 32GB See all hardware for StarCoder 7B

URL: https://willitrunai.com/can-run/starcoder-7b-on-m2-pro-32gb

⇱ StarCoder 7B on MacBook Pro M2 Pro 32GB? YES