Can I Run Bonsai 27B on MacBook Pro 16" (M4 Max, 128 GB)?

作者： Jakub Rusinowski · 最后更新： 2026年7月15日

Yes — comfortably

Yes, comfortably — 1-bit Bonsai 27B at 1-bit (binary, ~1.125 bpw) needs about 6.3 GB of the 96 GB usable on MacBook Pro 16" (M4 Max, 128 GB), leaving ~89.7 GB spare and running at ~53.8 tok/s (estimated), with room for about 262,144 tokens of context.

Confidence: medium · Recommended quantization: 1-bit (binary, ~1.125 bpw) · Estimated speed: ~53.8 tok/s

See what else this hardware can run →

购买此硬件 Apple MacBook Pro M5 Max — 128 GB unified memory · 614 GB/s 立即云端部署 RunPod 上的 RTX 4090 — 低至 $0.34/小时 · 价格核实于 2026-07

或在 Vast.ai 比较，低至 $0.35/小时 (typical low · varies)

作为亚马逊联盟成员，我们从符合条件的购买中获得收入。云 GPU 链接为推荐链接——我们可能获得佣金，您无需额外付费。

MacBook Pro 16" (M4 Max, 128 GB) — what it gives a model

Usable memory for models	96 GB
Memory bandwidth	546 GB/s
Form factor	Laptop
Operating system	macOS
Memory upgradeable	No — soldered

Bonsai 27B on MacBook Pro 16" (M4 Max, 128 GB): memory by quantization

Quant	Memory needed	Fits 96 GB?	Max context	Est. speed	Download
1-bit (binary, ~1.125 bpw)	6.3 GB	✓ Yes	256K	~53.8 tok/s	3.8 GB

Superseded model. Bonsai 27B has been superseded by Ternary Bonsai 2. This page is kept for reference; the newer family is a better starting point. View Ternary Bonsai 2 27B →

Which Bonsai 27B sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
1-bit Bonsai 27B	18.8 GB	✓ Fits	~16.6 tok/s
Ternary Bonsai 27B	18.8 GB	✓ Fits	~16.6 tok/s

What to watch out for

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 Max 128 GB limitations

Unified memory is soldered — the 128 GB decision is permanent and cannot be upgraded later.
Sustained decode speed on battery is materially below the plugged-in figures quoted here.

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 546 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.

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