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

Not always. MacBook Pro M4 Max 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.

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

YES — Runs Great

C49Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~17.0 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~35 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) — 17.0 GB, 34.7 tok/s, Runs well

17.0 GB required34.6 GB available

49% VRAM used

Fit status

Runs well

Decode

34.7 tok/s

TTFT

5573 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 Max 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: 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	176K
Coding	C	Runs well	34.7 tok/s	5573 ms	176K
Agentic Coding	C	Runs well	34.7 tok/s	8107 ms	176K
Reasoning	C	Runs well	34.7 tok/s	6587 ms	176K
RAG	C	Runs well	34.7 tok/s	10133 ms	176K

Quantization options

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

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

RTX PRO 5000 Blackwell 48GBBudget pick

1344 GB/s (+798)

Raises estimated decode speed by about 256%.123.4 tok/s decode

Raises estimated decode speed by about 256%.

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

~$4,999 MSRP

NVIDIA A100 40GBBest value

1555 GB/s (+1009)

Raises estimated decode speed by about 312%.142.8 tok/s decode

Raises estimated decode speed by about 312%.

~$10,000 MSRP

Frequently asked questions

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

Yes, MacBook Pro M4 Max 48GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 34.7 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 Max 48GB?

On MacBook Pro M4 Max 48GB, starcoder2 15b instruct v0.1 achieves approximately 34.7 tokens per second decode speed with a time-to-first-token of 5573ms using Q4_K_M quantization.

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

For coding workloads, starcoder2 15b instruct v0.1 on MacBook Pro M4 Max 48GB receives a C grade with 34.7 tok/s and 176K context.

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

On MacBook Pro M4 Max 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.

See all results for MacBook Pro M4 Max 48GB See all hardware for starcoder2 15b instruct v0.1

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