Is unified memory on MacBook Air M2 16GB as fast as VRAM for StarCoder2 15B?

Not always. MacBook Air M2 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 run on MacBook Air M2 16GB?

Q: What should I upgrade first if StarCoder2 15B feels slow on MacBook Air M2 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

D35Poor

Estimated from fit model

StarCoder2 15B needs ~13.5 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~6 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, 5.6 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

5.6 tok/s

TTFT

34848 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 on MacBook Air M2 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: 5.6 tok/s decode · 34.8s TTFT (warm) · 14 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 {ram} 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)	6.1 tok/s	17395 ms	4K
Coding	D	Very compromised	5.6 tok/s	34848 ms	4K
Agentic Coding	F	Too heavy	4.8 tok/s	58852 ms	4K
Reasoning	D	Very compromised (needs ~1.4 GB host RAM)	5.6 tok/s	41184 ms	4K
RAG	F	Too heavy	4.8 tok/s	73565 ms

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on MacBook Air M2 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	C52
NVFP4Best for your GPU

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

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)120 GB/s (+20)

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.

Raises estimated decode speed by about 55%.

~$799 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+16)120 GB/s (+20)

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.

Raises estimated decode speed by about 55%.

~$1,099 MSRP

MacBook Air M4 24GBApple upgrade

24 GB Unified (+8)120 GB/s (+20)

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.

Raises estimated decode speed by about 55%.

~$1,099 MSRP

RTX A4500 20GBBiggest leap

20 GB VRAM (+4)640 GB/s (+540)

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 875%.

~$2,000 MSRP

Frequently asked questions

Can MacBook Air M2 16GB run StarCoder2 15B?

Yes, MacBook Air M2 16GB can run StarCoder2 15B with a D grade (Very compromised). Expected decode speed: 5.6 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 13.5 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 Air M2 16GB?

On MacBook Air M2 16GB, StarCoder2 15B achieves approximately 5.6 tokens per second decode speed with a time-to-first-token of 34848ms using Q4_K_M quantization.

Can MacBook Air M2 16GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on MacBook Air M2 16GB receives a D grade with 5.6 tok/s and 4K context.

What context window can StarCoder2 15B use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, StarCoder2 15B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 15B feels slow on MacBook Air M2 16GB?

See all results for MacBook Air M2 16GB See all hardware for StarCoder2 15B

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