Can StarCoder 15B run on MacBook Pro M2 Pro 32GB?

YES — With Q3_K_S

B63Good

Estimated from fit model

StarCoder 15B needs ~26.7 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q3_K_S quantization, expect ~14 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Host offload

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.

StarCoder 15B at Q5_K_M needs 30.1 GB — too much for MacBook Pro M2 Pro 32GB (23.0 GB). Runs at Q3_K_S (26.7 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 30.1 GB, exceeds 23.0 GB available

30.1 GB required23.0 GB available

131% VRAM needed

7.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.1 tok/s

TTFT

21331 ms

Safe context

Memory

30.1 GB / 23.0 GB

Offload

20%

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom3.5 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 15B on MacBook Pro M2 Pro 32GB
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: 9.1 tok/s decode · 21.3s TTFT (warm) · 23 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	13.2 tok/s	7987 ms	8K
Coding	F	Too heavy	9.1 tok/s	21331 ms	8K
Agentic Coding	F	Too heavy	6.0 tok/s	47328 ms	8K
Reasoning	F	Too heavy	9.1 tok/s	25210 ms	8K
RAG	F	Too heavy	6.0 tok/s	59160 ms	8K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	A71
Q3_K_S	3	7.4 GB	Low	A72
NVFP4	4	8.4 GB	Medium	A73
Q4_K_M	4	9.2 GB	Medium	A74
Q5_K_M	5	10.8 GB	High	A75
Q6_K	6	12.3 GB	High	A76
Q8_0Best for your GPU	8	16.1 GB	Very High	A75
F16	16	30.7 GB	Maximum	F0

Get started

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

Run

lms load starcoder && lms server start

升级选项

能流畅运行 StarCoder 15B 的硬件

Mac mini M4 64GB经济之选

64 GB Unified (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.7.5 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

MacBook Pro M4 Pro 64GB最佳性价比

64 GB Unified (+32)273 GB/s (+73)

Makes the model fit on the accelerator instead of staying completely out of reach.18.3 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

MacBook Pro M3 Pro 36GBApple 升级

36 GB Unified (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.8.1 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

~$1,999 MSRP

NVIDIA A100 40GB最大飞跃

40 GB VRAM (+8)1555 GB/s (+1355)

Makes the model fit on the accelerator instead of staying completely out of reach.123.4 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$10,000 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run StarCoder 15B?

Yes, MacBook Pro M2 Pro 32GB can run StarCoder 15B at Q3_K_S quantization (Very compromised (needs ~1 GB host RAM)). The recommended Q5_K_M requires 30.1 GB which exceeds available memory, but at Q3_K_S it needs only 26.7 GB. Expected decode speed: 14.1 tok/s.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 30.1 GB at Q5_K_M quantization. On MacBook Pro M2 Pro 32GB, it fits at Q3_K_S using 26.7 GB.

What is the best quantization for StarCoder 15B?

The recommended quantization is Q5_K_M, but on MacBook Pro M2 Pro 32GB the best fitting quantization is Q3_K_S, which uses 26.7 GB.

What speed will StarCoder 15B run at on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, StarCoder 15B achieves approximately 14.1 tokens per second decode speed with a time-to-first-token of 13699ms using Q3_K_S quantization.

Can MacBook Pro M2 Pro 32GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on MacBook Pro M2 Pro 32GB receives a F grade with 9.1 tok/s and 8K context.

What context window can StarCoder 15B use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, StarCoder 15B can safely use up to 8K tokens of context at Q3_K_S quantization. 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 MacBook Pro M2 Pro 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for StarCoder 15B?

Not always. MacBook Pro M2 Pro 32GB 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 MacBook Pro M2 Pro 32GB See all hardware for StarCoder 15B

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