Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for starcoder2 15b instruct v0.1?

Not always. MacBook Pro M4 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.

Can starcoder2 15b instruct v0.1 run on MacBook Pro M4 Max 96GB?

YES — Runs Great

C46Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~22.2 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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, 34.7 tok/s, Runs well

22.2 GB required69.1 GB available

32% VRAM used

Fit status

Runs well

Decode

34.7 tok/s

TTFT

5573 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 instruct v0.1 on MacBook Pro M4 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: 34.7 tok/s decode · 5.6s TTFT (warm) · 87 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	34.7 tok/s	3040 ms	443K
Coding	C	Runs well	37.6 tok/s	5150 ms	443K
Agentic Coding	C	Runs well	34.7 tok/s	8107 ms	443K
Reasoning	C	Runs well	34.7 tok/s	6587 ms	443K
RAG	C	Runs well	34.7 tok/s	10133 ms	443K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	D40
Q3_K_S	3	7.4 GB	Low	C40
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs starcoder2 15b instruct v0.1 well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+32)800 GB/s (+254)

Raises estimated decode speed by about 46%.50.7 tok/s decode

Raises estimated decode speed by about 46%.

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 (+254)

Raises estimated decode speed by about 39%.48.1 tok/s decode

Raises estimated decode speed by about 39%.

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

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 96GB run starcoder2 15b instruct v0.1?

Yes, MacBook Pro M4 Max 96GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 37.6 tok/s.

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

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 22.2 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 Max 96GB?

On MacBook Pro M4 Max 96GB, starcoder2 15b instruct v0.1 achieves approximately 37.6 tokens per second decode speed with a time-to-first-token of 5150ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on MacBook Pro M4 Max 96GB receives a C grade with 37.6 tok/s and 443K context.

What context window can starcoder2 15b instruct v0.1 use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, starcoder2 15b instruct v0.1 can safely use up to 443K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 Max 96GB See all hardware for starcoder2 15b instruct v0.1

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