Can StarCoder 7B run on Mac mini M2 24GB?

YES — Tight Fit

A71Great

Estimated from fit model

StarCoder 7B needs ~15.1 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 15.1 GB, 15.2 tok/s, Tight fit

15.1 GB required17.3 GB available

87% VRAM used

Fit status

Tight fit

Decode

15.2 tok/s

TTFT

12718 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 Mac mini M2 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: 15.2 tok/s decode · 12.7s TTFT (warm) · 38 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	15.2 tok/s	6937 ms	8K
Coding	A	Tight fit	15.2 tok/s	12718 ms	8K
Agentic Coding	F	Too heavy	10.5 tok/s	26711 ms	8K
Reasoning	A	Tight fit	15.2 tok/s	15030 ms	8K
RAG	F	Too heavy	10.5 tok/s	33389 ms	8K

Quantization options

How StarCoder 7B (7B params) fits at each quantization level on Mac mini M2 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 Mac mini M2 24GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	12.7 tok/s
Magistral Small 2507	24B	B	3.7 tok/s
Devstral Small 2 24B Instruct	24B	B	3.7 tok/s
Qwen 3 14B	14B	S	8.2 tok/s
Qwen 3 8B	8B	S	14.3 tok/s

Frequently asked questions

Can Mac mini M2 24GB run StarCoder 7B?

Yes, Mac mini M2 24GB can run StarCoder 7B with a A grade (Tight fit). Expected decode speed: 15.2 tok/s.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 15.1 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder 7B?

The recommended quantization for StarCoder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder 7B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, StarCoder 7B achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12718ms using Q4_K_M quantization.

Can Mac mini M2 24GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on Mac mini M2 24GB receives a A grade with 15.2 tok/s and 8K context.

What context window can StarCoder 7B use on Mac mini M2 24GB?

On Mac mini M2 24GB, StarCoder 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for StarCoder 7B?

Not always. Mac mini M2 24GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac mini M2 24GB See all hardware for StarCoder 7B

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