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URL: https://willitrunai.com/can-run/starcoder-15b-on-m4-air-24gb

⇱ StarCoder 15B on MacBook Air M4 24GB? No — Alternatives

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

NO — Won't Fit

F0Won't run
○Estimated from fit model

StarCoder 15B needs ~29.2 GB but MacBook Air M4 24GB only has 17.3 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity
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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) — 29.2 GB, exceeds 17.3 GB available
29.2 GB required17.3 GB available
169% VRAM needed

11.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.9 tok/s

TTFT

50260 ms

Safe context

4K

Memory

29.2 GB / 17.3 GB

Offload

40%

Memory breakdown

Weights10.8 GB
KV Cache14.6 GB
Runtime1.2 GB
Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsStarCoder 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: 3.9 tok/s decode · 50.3s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 29.2 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy5.4 tok/s19688 ms4K
CodingFToo heavy3.9 tok/s50260 ms4K
Agentic CodingFToo heavy3.4 tok/s82996 ms4K
ReasoningFToo heavy3.9 tok/s59398 ms4K
RAGFToo heavy3.4 tok/s103745 ms4K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
5.9 GB
LowA74
Q3_K_S
3
7.4 GB
LowA75
NVFP4
4
8.4 GB
MediumA76
Q4_K_M
4
9.2 GB
MediumA76
Q5_K_M
5
10.8 GB
HighA76
Q6_KBest for your GPU
6
12.3 GB
HighA76
Q8_0
8
16.1 GB
Very HighF0
F16
16
30.7 GB
MaximumF0

Upgrade options

Hardware that runs StarCoder 15B well

Mac mini M4 64GBBudget pick
64 GB Unified (+40)
A
Makes the model fit on the accelerator instead of staying completely out of reach.7.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value
64 GB Unified (+40)273 GB/s (+153)
A
Makes the model fit on the accelerator instead of staying completely out of reach.18.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,599 MSRP

MacBook Pro M3 Pro 36GBApple upgrade
36 GB Unified (+12)150 GB/s (+30)
B
Makes the model fit on the accelerator instead of staying completely out of reach.8.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 108%.

~$1,999 MSRP

👁 NVIDIA
NVIDIA A100 40GBBiggest leap
40 GB VRAM (+16)1555 GB/s (+1435)
A
Makes the model fit on the accelerator instead of staying completely out of reach.123.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$10,000 MSRP

Frequently asked questions

See all results for MacBook Air M4 24GBSee all hardware for StarCoder 15B