Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for starcoder2 15b instruct v0.1?

Not always. MacBook Pro M3 Max 128GB 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 M3 Max 128GB?

YES — Runs Great

C43Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~25.6 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~26 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) — 25.6 GB, 26.2 tok/s, Runs well

25.6 GB required92.2 GB available

28% VRAM used

Fit status

Runs well

Decode

26.2 tok/s

TTFT

7381 ms

Safe context

622K

Memory

25.6 GB / 92.2 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on MacBook Pro M3 Max 128GB
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: 26.2 tok/s decode · 7.4s TTFT (warm) · 66 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	26.2 tok/s	4026 ms	622K
Coding	C	Runs well	26.2 tok/s	7381 ms	622K
Agentic Coding	C	Runs well	26.2 tok/s	10736 ms	622K
Reasoning	C	Runs well	26.2 tok/s	8723 ms	622K
RAG	C	Runs well	26.2 tok/s	13420 ms	622K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	D39
Q3_K_S	3	7.4 GB	Low	D39
NVFP4	4

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

Upgrade options

Hardware that runs starcoder2 15b instruct v0.1 well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)819 GB/s (+419)

Raises estimated decode speed by about 132%.60.9 tok/s decode

Raises estimated decode speed by about 132%.

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

~$6,999 MSRP

AMD Instinct MI350X 288GBBest value

288 GB VRAM (+160)8000 GB/s (+7600)

Raises estimated decode speed by about 702%.210 tok/s decode

Raises estimated decode speed by about 702%.

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

~$8,000 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 128GB run starcoder2 15b instruct v0.1?

Yes, MacBook Pro M3 Max 128GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 26.2 tok/s.

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

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 25.6 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 M3 Max 128GB?

On MacBook Pro M3 Max 128GB, starcoder2 15b instruct v0.1 achieves approximately 26.2 tokens per second decode speed with a time-to-first-token of 7381ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 128GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on MacBook Pro M3 Max 128GB receives a C grade with 26.2 tok/s and 622K context.

What context window can starcoder2 15b instruct v0.1 use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, starcoder2 15b instruct v0.1 can safely use up to 622K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M3 Max 128GB See all hardware for starcoder2 15b instruct v0.1

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