Can starcoder2 15b instruct v0.1 run on MacBook Pro M4 Pro 48GB?

YES — Runs Great

C48Usable

Estimated — low-sample bucket· few comparable runs

starcoder2 15b instruct v0.1 needs ~17.0 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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) — 17.0 GB, 21.2 tok/s, Runs well

17.0 GB required34.6 GB available

49% VRAM used

Fit status

Runs well

Decode

21.2 tok/s

TTFT

9120 ms

Safe context

176K

Memory

17.0 GB / 34.6 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on MacBook Pro M4 Pro 48GB
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: 21.2 tok/s decode · 9.1s TTFT (warm) · 53 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	23.0 tok/s	4597 ms	176K
Coding	C	Runs well	23.0 tok/s	8427 ms	176K
Agentic Coding	C	Runs well	23.0 tok/s	12257 ms	176K
Reasoning	C	Runs well	23.0 tok/s	9959 ms	176K
RAG	C	Runs well	23.0 tok/s	15322 ms	176K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C43
Q3_K_S	3	7.4 GB	Low	C44
NVFP4	4	8.4 GB	Medium	C44
Q4_K_M	4	9.2 GB	Medium	C44
Q5_K_M	5	10.8 GB	High	C45
Q6_K	6	12.3 GB	High	C46
Q8_0Best for your GPU	8	16.1 GB	Very High	C48
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.

Run

lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server start

アップグレードオプション

starcoder2 15b instruct v0.1を快適に動かすハードウェア

Mac Studio M2 Ultra 64GBコスパ重視

64 GB Unified (+16)800 GB/s (+527)

Raises estimated decode speed by about 139%.50.7 tok/sデコード

Raises estimated decode speed by about 139%.

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

〜$3,999 MSRP

Mac Studio M1 Ultra 64GBコスパ最良

64 GB Unified (+16)800 GB/s (+527)

Raises estimated decode speed by about 127%.48.1 tok/sデコード

Raises estimated decode speed by about 127%.

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

〜$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run starcoder2 15b instruct v0.1?

Yes, MacBook Pro M4 Pro 48GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 23.0 tok/s.

How much VRAM does starcoder2 15b instruct v0.1 need?

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 17.0 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b instruct v0.1?

The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b instruct v0.1 run at on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, starcoder2 15b instruct v0.1 achieves approximately 23.0 tokens per second decode speed with a time-to-first-token of 8427ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on MacBook Pro M4 Pro 48GB receives a C grade with 23.0 tok/s and 176K context.

What context window can starcoder2 15b instruct v0.1 use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, starcoder2 15b instruct v0.1 can safely use up to 176K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for starcoder2 15b instruct v0.1?

Not always. MacBook Pro M4 Pro 48GB 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 M4 Pro 48GB See all hardware for starcoder2 15b instruct v0.1

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