Can StarCoder 15B run on Mac mini M2 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

StarCoder 15B needs ~29.2 GB but Mac mini M2 24GB only has 17.3 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity

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) — 29.2 GB, exceeds 17.3 GB available

29.2 GB required17.3 GB available

169% VRAM needed

11.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.1 tok/s

TTFT

61729 ms

Safe context

Memory

29.2 GB / 17.3 GB

Offload

40%

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 15B 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: 3.1 tok/s decode · 61.7s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 29.2 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.4 tok/s	24181 ms	4K
Coding	F	Too heavy	3.1 tok/s	61729 ms	4K
Agentic Coding	F	Too heavy	2.8 tok/s	101937 ms	4K
Reasoning	F	Too heavy	3.1 tok/s	72953 ms	4K
RAG	F	Too heavy	2.8 tok/s	127421 ms	4K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	A74
Q3_K_S	3	7.4 GB	Low	A75
NVFP4	4	8.4 GB	Medium	A76
Q4_K_M	4	9.2 GB	Medium	A76
Q5_K_M	5	10.8 GB	High	A76
Q6_KBest for your GPU	6	12.3 GB	High	A76
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien StarCoder 15B

Mac mini M4 64GBOpción económica

64 GB Unified (+40)120 GB/s (+20)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.7.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBMejor relación calidad-precio

64 GB Unified (+40)273 GB/s (+173)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.18.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,599 MSRP

Mac Studio M2 Ultra 64GBMejora Apple

64 GB Unified (+40)800 GB/s (+700)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.43.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run StarCoder 15B?

No, StarCoder 15B requires more memory than Mac mini M2 24GB provides.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 29.2 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder 15B?

The recommended quantization for StarCoder 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder 15B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, StarCoder 15B achieves approximately 3.1 tokens per second decode speed with a time-to-first-token of 61729ms using Q5_K_M quantization.

Can Mac mini M2 24GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on Mac mini M2 24GB receives a F grade with 3.1 tok/s and 4K context.

What context window can StarCoder 15B use on Mac mini M2 24GB?

On Mac mini M2 24GB, StarCoder 15B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder 15B feels slow on Mac mini M2 24GB?

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Is unified memory on Mac mini M2 24GB as fast as VRAM for StarCoder 15B?

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 15B

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