Can I Run Nemotron Cascade 2 on MacBook Pro 14" (M4 Pro, 24 GB)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 marca 2026

Yes

Yes — Nemotron Cascade 2 30B at Q3_K_M needs about 15.4 GB of the 18 GB usable on MacBook Pro 14" (M4 Pro, 24 GB) (~2.6 GB spare), at ~10.6 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q3_K_M · Estimated speed: ~10.6 tok/s

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MacBook Pro 14" (M4 Pro, 24 GB) — what it gives a model

Usable memory for models	18 GB
Memory bandwidth	273 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered
Price	$1,999 (lib/data/laptops.ts (street price), checked 2026-07-06)

Nemotron Cascade 2 on MacBook Pro 14" (M4 Pro, 24 GB): memory by quantization

Quant	Memory needed	Fits 18 GB?	Max context	Est. speed	Download
F16	62.6 GB	✗ No	—	—	60 GB
Q8_0	34.5 GB	✗ No	—	—	31.9 GB
Q6_K	27.2 GB	✗ No	—	—	24.6 GB
Q5_K_M	23.8 GB	✗ No	—	—	21.3 GB
Q4_K_M	20.7 GB	✗ No	—	—	18.1 GB
Q3_K_M	15.4 GB	✓ Yes	16K	~10.6 tok/s	12.8 GB
Q2_K	12.4 GB	✓ Yes	32K	~13.4 tok/s	9.9 GB

Which Nemotron Cascade 2 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Nemotron Cascade 2 70B	45.4 GB	✗ Too large	—
Nemotron Cascade 2 30B	20.7 GB	✗ Too large	—

What to watch out for

Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
2 larger variants of Nemotron Cascade 2 do not fit and would need CPU offload or different hardware.
This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
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 M4 Pro 24 GB limitations

Unified memory is soldered and cannot be upgraded after purchase.
24 GB is the practical ceiling for a single mid-size model plus a long context.

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.
24 GB unified memory at 273 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 inferred from the parameter count because this model does not publish its full attention configuration.

FAQ