Can StarCoder 7B run on MacBook Pro M4 Pro 24GB?

YES — Tight Fit

A75Great

▲Estimated — low-sample bucket· few comparable runs

StarCoder 7B needs ~15.1 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~45 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) — 15.1 GB, 45.3 tok/s, Tight fit

15.1 GB required17.3 GB available

87% VRAM used

Fit status

Tight fit

Decode

45.3 tok/s

TTFT

4275 ms

Safe context

Memory

15.1 GB / 17.3 GB

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsStarCoder 7B 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: 45.3 tok/s decode · 4.3s TTFT (warm) · 113 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	45.3 tok/s	2332 ms	8K
Coding	A	Tight fit	45.3 tok/s	4275 ms	8K
Agentic Coding	F	Too heavy	31.4 tok/s	8978 ms	8K
Reasoning	A	Tight fit	45.3 tok/s	5052 ms	8K
RAG	F	Too heavy	31.4 tok/s	11222 ms	8K

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B70
Q3_K_S	3	3.4 GB	Low	A70
NVFP4	4	3.9 GB	Medium	A70
Q4_K_M	4	4.3 GB	Medium	A71
Q5_K_M	5	5.0 GB	High	A71
Q6_K	6	5.7 GB	High	A72
Q8_0Best for your GPU	8	7.5 GB	Very High	A74
F16	16	14.3 GB	Maximum	F0

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 M4 Pro 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	37.9 tok/s
👁 Mistral Magistral Small 2507	24B	A	17.8 tok/s
👁 Mistral Devstral Small 2 24B Instruct	24B	A	17.8 tok/s
👁 Alibaba Qwen 3 14B	14B	S	23.4 tok/s
👁 Alibaba Qwen 3 8B	8B	S	42.6 tok/s

Frequently asked questions

See all results for MacBook Pro M4 Pro 24GB See all hardware for StarCoder 7B

URL: https://willitrunai.com/can-run/starcoder-7b-on-m4-pro-24gb

⇱ StarCoder 7B on MacBook Pro M4 Pro 24GB? TIGHT FIT