Can starcoder2 15b i1 run on MacBook Pro M4 Max 36GB?

Q: Is unified memory on MacBook Pro M4 Max 36GB as fast as VRAM for starcoder2 15b i1?

Not always. MacBook Pro M4 Max 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.

YES — Runs Great

C51Usable

Estimated from fit model

starcoder2 15b i1 needs ~15.7 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~30 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) — 15.7 GB, 29.6 tok/s, Runs well

15.7 GB required25.9 GB available

61% VRAM used

Fit status

Runs well

Decode

29.6 tok/s

TTFT

6531 ms

Safe context

109K

Memory

15.7 GB / 25.9 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsstarcoder2 15b i1 on MacBook Pro M4 Max 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: 29.6 tok/s decode · 6.5s TTFT (warm) · 74 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	29.6 tok/s	3563 ms	109K
Coding	C	Runs well	29.6 tok/s	6531 ms	109K
Agentic Coding	C	Runs well	29.6 tok/s	9500 ms	109K
Reasoning	C	Runs well	29.6 tok/s	7719 ms	109K
RAG	C	Runs well	29.6 tok/s	11875 ms	109K

Quantization options

How starcoder2 15b i1 (15B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C45
Q3_K_S	3	7.4 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run starcoder2 15b i1 on your machine.

Run

lms load hf-mradermacher--starcoder2-15b-i1-gguf && lms server start

Upgrade options

Hardware that runs starcoder2 15b i1 well

RTX 5090 32GBBudget pick

1792 GB/s (+1382)

Raises estimated decode speed by about 319%.124 tok/s decode

Raises estimated decode speed by about 319%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

896 GB/s (+486)

Raises estimated decode speed by about 178%.82.3 tok/s decode

Raises estimated decode speed by about 178%.

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

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 36GB run starcoder2 15b i1?

Yes, MacBook Pro M4 Max 36GB can run starcoder2 15b i1 with a C grade (Runs well). Expected decode speed: 29.6 tok/s.

How much VRAM does starcoder2 15b i1 need?

starcoder2 15b i1 (15B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b i1?

The recommended quantization for starcoder2 15b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b i1 run at on MacBook Pro M4 Max 36GB?

On MacBook Pro M4 Max 36GB, starcoder2 15b i1 achieves approximately 29.6 tokens per second decode speed with a time-to-first-token of 6531ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 36GB run starcoder2 15b i1 for coding?

For coding workloads, starcoder2 15b i1 on MacBook Pro M4 Max 36GB receives a C grade with 29.6 tok/s and 109K context.

What context window can starcoder2 15b i1 use on MacBook Pro M4 Max 36GB?

On MacBook Pro M4 Max 36GB, starcoder2 15b i1 can safely use up to 109K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 36GB as fast as VRAM for starcoder2 15b i1?

See all results for MacBook Pro M4 Max 36GB See all hardware for starcoder2 15b i1

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