Can StarCoder2 7B run on Mac mini M2 24GB?

YES — Runs Great

C46Usable

Estimated from fit model

StarCoder2 7B needs ~8.3 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~17 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) — 8.3 GB, 16.6 tok/s, Runs well

8.3 GB required17.3 GB available

48% VRAM used

Fit status

Runs well

Decode

16.6 tok/s

TTFT

11650 ms

Safe context

16K

Memory

8.3 GB / 17.3 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsStarCoder2 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: 16.6 tok/s decode · 11.7s TTFT (warm) · 42 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	C	Runs well	16.6 tok/s	6355 ms	16K
Coding	C	Runs well	16.6 tok/s	11650 ms	16K
Agentic Coding	C	Runs well	16.6 tok/s	16946 ms	16K
Reasoning	C	Runs well	16.6 tok/s	13768 ms	16K
RAG	C	Runs well	16.6 tok/s	21182 ms	16K

Quantization options

How StarCoder2 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	C45
Q3_K_S	3	3.4 GB	Low	C46
NVFP4	4	3.9 GB	Medium	C46
Q4_K_M	4	4.3 GB	Medium	C47
Q5_K_M	5	5.0 GB	High	C47
Q6_K	6	5.7 GB	High	C48
Q8_0Best for your GPU	8	7.5 GB	Very High	C50
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load starcoder2-7b && lms server start

Upgrade-Optionen

Hardware, die StarCoder2 7B gut ausführt

MacBook Pro M2 Max 32GBGünstige Wahl

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 257%.59.3 tok/s Dekodierung

Raises estimated decode speed by about 257%.

Adds memory headroom for longer context windows and future model growth.

ca. $1,999 MSRP

MacBook Pro M2 Pro 32GBBestes Preis-Leistungs-Verhältnis

32 GB Unified (+8)200 GB/s (+100)

Raises estimated decode speed by about 116%.35.8 tok/s Dekodierung

Raises estimated decode speed by about 116%.

Adds memory headroom for longer context windows and future model growth.

ca. $1,999 MSRP

Mac Studio M2 Ultra 64GBApple-Upgrade

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

Raises estimated decode speed by about 490%.98 tok/s Dekodierung

Raises estimated decode speed by about 490%.

Adds memory headroom for longer context windows and future model growth.

ca. $3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run StarCoder2 7B?

Yes, Mac mini M2 24GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 16.6 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, StarCoder2 7B achieves approximately 16.6 tokens per second decode speed with a time-to-first-token of 11650ms using Q4_K_M quantization.

Can Mac mini M2 24GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on Mac mini M2 24GB receives a C grade with 16.6 tok/s and 16K context.

What context window can StarCoder2 7B use on Mac mini M2 24GB?

On Mac mini M2 24GB, StarCoder2 7B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for StarCoder2 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 StarCoder2 7B

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