Can StarCoder2 15B run on MacBook Air M4 24GB?

Q: Is unified memory on MacBook Air M4 24GB as fast as VRAM for StarCoder2 15B?

Not always. MacBook Air M4 24GB 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.

YES — Tight Fit

C48Usable

Estimated — low-sample bucket· few comparable runs

StarCoder2 15B needs ~15.5 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q5_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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

Q5_K_M (High quality) — 15.5 GB, 8.2 tok/s, Tight fit

15.5 GB required17.3 GB available

90% VRAM used

Fit status

Tight fit

Decode

8.2 tok/s

TTFT

23521 ms

Safe context

16K

Memory

15.5 GB / 17.3 GB

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on MacBook Air M4 24GB
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: 8.2 tok/s decode · 23.5s TTFT (warm) · 21 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	Tight fit	8.2 tok/s	12941 ms	16K
Coding	C	Tight fit	8.2 tok/s	23725 ms	16K
Agentic Coding	C	Runs with offload	8.2 tok/s	34510 ms	16K
Reasoning	C	Tight fit	8.2 tok/s	28039 ms	16K
RAG	C	Runs with offload	8.2 tok/s	43137 ms	16K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C50
Q3_K_S	3	7.4 GB	Low	C52
NVFP4	4

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bigcode/starcoder2-15b" \
  --hf-file "starcoder2-15b-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs StarCoder2 15B well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.8.2 tok/s decode

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

~$799 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.8.2 tok/s decode

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

~$1,099 MSRP

MacBook Pro M2 Max 32GBApple upgrade

32 GB Unified (+8)400 GB/s (+280)

Raises estimated decode speed by about 191%.23.9 tok/s decode

Raises estimated decode speed by about 191%.

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

~$1,999 MSRP

Frequently asked questions

Can MacBook Air M4 24GB run StarCoder2 15B?

Yes, MacBook Air M4 24GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 8.2 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 15.5 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on MacBook Air M4 24GB?

On MacBook Air M4 24GB, StarCoder2 15B achieves approximately 8.2 tokens per second decode speed with a time-to-first-token of 23725ms using Q5_K_M quantization.

Can MacBook Air M4 24GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on MacBook Air M4 24GB receives a C grade with 8.2 tok/s and 16K context.

What context window can StarCoder2 15B use on MacBook Air M4 24GB?

On MacBook Air M4 24GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M4 24GB as fast as VRAM for StarCoder2 15B?

See all results for MacBook Air M4 24GB See all hardware for StarCoder2 15B

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