Can StarCoder2 15B run on Mac mini M2 24GB?

YES — Tight Fit

C47Usable

Estimated from fit model

StarCoder2 15B needs ~15.5 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q5_K_M quantization, expect ~7 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

Q5_K_M (High quality) — 15.5 GB, 6.7 tok/s, Tight fit

15.5 GB required17.3 GB available

90% VRAM used

Fit status

Tight fit

Decode

6.7 tok/s

TTFT

28889 ms

Safe context

16K

Memory

15.5 GB / 17.3 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsStarCoder2 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: 6.7 tok/s decode · 28.9s TTFT (warm) · 17 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	6.7 tok/s	15757 ms	16K
Coding	C	Tight fit	6.7 tok/s	28889 ms	16K
Agentic Coding	C	Runs with offload	6.7 tok/s	42020 ms	16K
Reasoning	C	Tight fit	6.7 tok/s	34141 ms	16K
RAG	C	Runs with offload	6.7 tok/s	52525 ms	16K

Quantization options

How StarCoder2 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	C50
Q3_K_S	3	7.4 GB	Low	C52
NVFP4	4	8.4 GB	Medium	C53
Q4_K_M	4	9.2 GB	Medium	C53
Q5_K_M	5	10.8 GB	High	C52
Q6_KBest for your GPU	6	12.3 GB	High	C52
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bigcode/starcoder2-15b" \
  --hf-file "starcoder2-15b-Q5_K_M.gguf" \
  -c 4096 -ngl 99

升级选项

能流畅运行 StarCoder2 15B 的硬件

MacBook Pro M4 32GB经济之选

32 GB Unified (+8)120 GB/s (+20)

Adds memory headroom for longer context windows and future model growth.8.2 tok/s 解码

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

~$799 MSRP

Mac mini M4 32GB最佳性价比

32 GB Unified (+8)120 GB/s (+20)

Adds memory headroom for longer context windows and future model growth.8.2 tok/s 解码

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

~$1,099 MSRP

RTX 4090 24GB最大飞跃

1008 GB/s (+908)

Raises estimated decode speed by about 1106%.80.8 tok/s 解码

Raises estimated decode speed by about 1106%.

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

~$1,599 MSRP

Mac Studio M2 Ultra 64GBApple 升级

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

Raises estimated decode speed by about 613%.47.8 tok/s 解码

Raises estimated decode speed by about 613%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run StarCoder2 15B?

Yes, Mac mini M2 24GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 6.7 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 15.5 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, StarCoder2 15B achieves approximately 6.7 tokens per second decode speed with a time-to-first-token of 28889ms using Q5_K_M quantization.

Can Mac mini M2 24GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on Mac mini M2 24GB receives a C grade with 6.7 tok/s and 16K context.

What context window can StarCoder2 15B use on Mac mini M2 24GB?

On Mac mini M2 24GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 15B feels slow on Mac mini M2 24GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on Mac mini M2 24GB as fast as VRAM for StarCoder2 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 StarCoder2 15B

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