Can I Run StarCoder 2 on MacBook Air (M4, 16 GB)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 28 lutego 2024

Yes

Yes — StarCoder 2 15B at Q4_K_M needs about 10.8 GB of the 12 GB usable on MacBook Air (M4, 16 GB) (~1.2 GB spare), at ~6.6 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q4_K_M · Estimated speed: ~6.6 tok/s

Buy This HardwareIntel Arc B580 12GB — 12 GB VRAM · 190 W board power Deploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

MacBook Air (M4, 16 GB) — what it gives a model

Usable memory for models	12 GB
Memory bandwidth	120 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered

StarCoder 2 on MacBook Air (M4, 16 GB): memory by quantization

Quant	Memory needed	Fits 12 GB?	Max context	Est. speed	Download
F16	32.5 GB	✗ No	—	—	31 GB
Q8_0	17.9 GB	✗ No	—	—	16.5 GB
Q6_K	14.2 GB	✗ No	—	—	12.7 GB
Q5_K_M	12.5 GB	✗ No	—	—	11 GB
Q4_K_M	10.8 GB	✓ Yes	16K	~6.6 tok/s	9.4 GB
Q3_K_M	8.1 GB	✓ Yes	16K	~9.2 tok/s	6.6 GB
Q2_K	6.6 GB	✓ Yes	16K	~11.7 tok/s	5.1 GB

Which StarCoder 2 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
StarCoder 2 15B	10.8 GB	✓ Fits	~6.6 tok/s
StarCoder 2 7B	5.7 GB	✓ Fits	~13.7 tok/s
StarCoder 2 3B	2.9 GB	✓ Fits	~30.8 tok/s

What to watch out for

Only ~1.2 GB of headroom at Q4_K_M: a longer context or a second application can push this into swapping.
Memory on this machine is not upgradeable, so the configuration you buy is the ceiling for every model you will ever run on it.

MacBook Air M4 16 GB limitations

Fanless: sustained generation throttles on long runs in a way the same chip in a MacBook Pro does not.
16 GB unified memory is shared with the OS and every open app.

Recommended setup

Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
16 GB unified memory at 120 GB/s, shared between CPU and GPU.
macOS reserves a share of unified memory for the system, so not all of it is available to a model.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is computed from this model's published attention configuration.

FAQ