Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for starcoder2 15b i1?

Not always. MacBook Pro M2 Pro 16GB 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 i1 run on MacBook Pro M2 Pro 16GB?

Q: What should I upgrade first if starcoder2 15b i1 feels slow on MacBook Pro M2 Pro 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

D37Poor

Estimated from fit model

starcoder2 15b i1 needs ~13.5 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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) — 13.5 GB, 12.0 tok/s, Very compromised (needs ~1.4 GB host RAM)

13.5 GB required11.5 GB available

117% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.4 GB host RAM)

Decode

12.0 tok/s

TTFT

16180 ms

Safe context

Memory

13.5 GB / 11.5 GB

Offload

10%

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsstarcoder2 15b i1 on MacBook Pro M2 Pro 16GB
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: 12.0 tok/s decode · 16.2s TTFT (warm) · 30 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	D	Very compromised (needs ~0.8 GB host RAM)	13.1 tok/s	8076 ms	4K
Coding	D	Very compromised (needs ~1.4 GB host RAM)	12.0 tok/s	16180 ms	4K
Agentic Coding	F	Too heavy	10.3 tok/s	27324 ms	4K
Reasoning	D	Very compromised (needs ~1.4 GB host RAM)	12.0 tok/s	19121 ms	4K
RAG	F	Too heavy	10.3 tok/s	34155 ms

Quantization options

How starcoder2 15b i1 (15B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C52
Q3_K_S	3	7.4 GB	Low	C51
NVFP4Best for your GPU

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

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.8.7 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$799 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.8.7 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$1,099 MSRP

MacBook Air M4 24GBApple upgrade

24 GB Unified (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.8.7 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$1,099 MSRP

RTX A4500 20GBBiggest leap

20 GB VRAM (+4)640 GB/s (+440)

Removes host-memory offload, which is usually the single biggest latency and throughput win.54.6 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 355%.

~$2,000 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run starcoder2 15b i1?

Yes, MacBook Pro M2 Pro 16GB can run starcoder2 15b i1 with a D grade (Very compromised (needs ~1.4 GB host RAM)). Expected decode speed: 12.0 tok/s.

How much VRAM does starcoder2 15b i1 need?

starcoder2 15b i1 (15B parameters) requires approximately 13.5 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 M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, starcoder2 15b i1 achieves approximately 12.0 tokens per second decode speed with a time-to-first-token of 16180ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run starcoder2 15b i1 for coding?

For coding workloads, starcoder2 15b i1 on MacBook Pro M2 Pro 16GB receives a D grade with 12.0 tok/s and 4K context.

What context window can starcoder2 15b i1 use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, starcoder2 15b i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M2 Pro 16GB See all hardware for starcoder2 15b i1

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