Can StarCoder2 15B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

C45Usable

Estimated from fit model

StarCoder2 15B needs ~22.2 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~25 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) — 22.2 GB, 25.4 tok/s, Runs well

22.2 GB required69.1 GB available

32% VRAM used

Fit status

Runs well

Decode

25.4 tok/s

TTFT

7636 ms

Safe context

443K

Memory

22.2 GB / 69.1 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on MacBook Pro M2 Max 96GB
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: 25.4 tok/s decode · 7.6s TTFT (warm) · 63 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	25.4 tok/s	4165 ms	443K
Coding	C	Runs well	25.4 tok/s	7636 ms	443K
Agentic Coding	C	Runs well	25.4 tok/s	11106 ms	443K
Reasoning	C	Runs well	25.4 tok/s	9024 ms	443K
RAG	C	Runs well	25.4 tok/s	13883 ms	443K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C40
Q3_K_S	3	7.4 GB	Low	C40
NVFP4	4	8.4 GB	Medium	C41
Q4_K_M	4	9.2 GB	Medium	C41
Q5_K_M	5	10.8 GB	High	C41
Q6_K	6	12.3 GB	High	C41
Q8_0	8	16.1 GB	Very High	C42
F16Best for your GPU	16	30.7 GB	Maximum	C45

Get started

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

Run

lms load hf-second-state--starcoder2-15b-gguf && lms server start

Upgrade options

Hardware that runs StarCoder2 15B well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 100%.50.7 tok/s decode

Raises estimated decode speed by about 100%.

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

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 89%.48.1 tok/s decode

Raises estimated decode speed by about 89%.

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

~$3,999 MSRP

MacBook Pro M4 Max 128GBApple upgrade

128 GB Unified (+32)546 GB/s (+146)

Raises estimated decode speed by about 37%.34.7 tok/s decode

Raises estimated decode speed by about 37%.

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

~$4,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 96GB run StarCoder2 15B?

Yes, MacBook Pro M2 Max 96GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 25.4 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 22.2 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, StarCoder2 15B achieves approximately 25.4 tokens per second decode speed with a time-to-first-token of 7636ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on MacBook Pro M2 Max 96GB receives a C grade with 25.4 tok/s and 443K context.

What context window can StarCoder2 15B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, StarCoder2 15B can safely use up to 443K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for StarCoder2 15B?

Not always. MacBook Pro M2 Max 96GB 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 Max 96GB See all hardware for StarCoder2 15B

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<iframe src="https://willitrunai.com/embed/hf-second-state--starcoder2-15b-gguf-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

25.4 tok/s

4165 ms

443K

Coding

Runs well

25.4 tok/s

7636 ms

443K

Agentic Coding

Runs well

25.4 tok/s

11106 ms

443K

Reasoning

Runs well

25.4 tok/s

9024 ms

443K

RAG

Runs well

25.4 tok/s

13883 ms

443K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C40
Q3_K_S	3	7.4 GB	Low	C40
NVFP4	4	8.4 GB	Medium	C41
Q4_K_M	4	9.2 GB	Medium	C41
Q5_K_M	5	10.8 GB	High	C41
Q6_K	6	12.3 GB	High	C41
Q8_0	8	16.1 GB	Very High	C42
F16Best for your GPU	16	30.7 GB	Maximum	C45

Frequently asked questions

Can MacBook Pro M2 Max 96GB run StarCoder2 15B?

Yes, MacBook Pro M2 Max 96GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 25.4 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 22.2 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, StarCoder2 15B achieves approximately 25.4 tokens per second decode speed with a time-to-first-token of 7636ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on MacBook Pro M2 Max 96GB receives a C grade with 25.4 tok/s and 443K context.

What context window can StarCoder2 15B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, StarCoder2 15B can safely use up to 443K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for StarCoder2 15B?