Can StarCoder2 15B run on MacBook Pro M3 Pro 36GB?

YES — Runs Great

C50Usable

Estimated from fit model

StarCoder2 15B needs ~16.8 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q5_K_M quantization, expect ~11 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

Q5_K_M (High quality) — 16.8 GB, 11.3 tok/s, Runs well

16.8 GB required25.9 GB available

65% VRAM used

Fit status

Runs well

Decode

11.3 tok/s

TTFT

17148 ms

Safe context

16K

Memory

16.8 GB / 25.9 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on MacBook Pro M3 Pro 36GB
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: 11.3 tok/s decode · 17.1s TTFT (warm) · 28 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	11.3 tok/s	9354 ms	16K
Coding	C	Runs well	11.3 tok/s	17148 ms	16K
Agentic Coding	C	Runs well	11.3 tok/s	24943 ms	16K
Reasoning	C	Runs well	11.3 tok/s	20266 ms	16K
RAG	C	Runs well	11.3 tok/s	31179 ms	16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C47
Q3_K_S	3	7.4 GB	Low	C48
NVFP4	4	8.4 GB	Medium	C48
Q4_K_M	4	9.2 GB	Medium	C49
Q5_K_M	5	10.8 GB	High	C50
Q6_K	6	12.3 GB	High	C51
Q8_0Best for your GPU	8	16.1 GB	Very High	C51
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

アップグレードオプション

StarCoder2 15Bを快適に動かすハードウェア

RTX 5090 32GBコスパ最良

1792 GB/s (+1642)

Raises estimated decode speed by about 935%.117 tok/sデコード

Raises estimated decode speed by about 935%.

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

〜$1,999 MSRP

MacBook Pro M4 Max 48GBコスパ重視

48 GB Unified (+12)546 GB/s (+396)

Raises estimated decode speed by about 190%.32.8 tok/sデコード

Raises estimated decode speed by about 190%.

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

〜$2,499 MSRP

Frequently asked questions

Can MacBook Pro M3 Pro 36GB run StarCoder2 15B?

Yes, MacBook Pro M3 Pro 36GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 11.3 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 16.8 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 MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, StarCoder2 15B achieves approximately 11.3 tokens per second decode speed with a time-to-first-token of 17148ms using Q5_K_M quantization.

Can MacBook Pro M3 Pro 36GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on MacBook Pro M3 Pro 36GB receives a C grade with 11.3 tok/s and 16K context.

What context window can StarCoder2 15B use on MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, 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.

Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for StarCoder2 15B?

Not always. MacBook Pro M3 Pro 36GB 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 M3 Pro 36GB See all hardware for StarCoder2 15B

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