Can I Run StarCoder 2 on MacBook Pro 16" (M5 Max, 128 GB)?

Written by Jakub Rusinowski · Last updated February 28, 2024

Yes — comfortably

Yes, comfortably — StarCoder 2 15B at Q8_0 needs about 17.9 GB of the 96 GB usable on MacBook Pro 16" (M5 Max, 128 GB), leaving ~78.1 GB spare and running at ~18.9 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~18.9 tok/s

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MacBook Pro 16" (M5 Max, 128 GB) — what it gives a model

Usable memory for models	96 GB
Memory bandwidth	614 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered

StarCoder 2 on MacBook Pro 16" (M5 Max, 128 GB): memory by quantization

Quant	Memory needed	Fits 96 GB?	Max context	Est. speed	Download
F16	32.5 GB	✓ Yes	16K	~10.4 tok/s	31 GB
Q8_0	17.9 GB	✓ Yes	16K	~18.9 tok/s	16.5 GB
Q6_K	14.2 GB	✓ Yes	16K	~23.9 tok/s	12.7 GB
Q5_K_M	12.5 GB	✓ Yes	16K	~27.3 tok/s	11 GB
Q4_K_M	10.8 GB	✓ Yes	16K	~31.4 tok/s	9.4 GB
Q3_K_M	8.1 GB	✓ Yes	16K	~42.3 tok/s	6.6 GB
Q2_K	6.6 GB	✓ Yes	16K	~52.3 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	~31.4 tok/s
StarCoder 2 7B	5.7 GB	✓ Fits	~59.8 tok/s
StarCoder 2 3B	2.9 GB	✓ Fits	~114.2 tok/s

What to watch out for

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 Pro M5 Max 128 GB limitations

Unified memory is soldered and cannot be upgraded after purchase.

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.
128 GB unified memory at 614 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.

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