Can I Run BitNet b1.58 on MacBook Pro 16" (M4 Max, 128 GB)?
作者： Jakub Rusinowski · 最后更新： 2024年3月1日
Yes — comfortably
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 96 GB usable on MacBook Pro 16" (M4 Max, 128 GB), leaving ~90.8 GB spare and running at ~61.6 tok/s (estimated).
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~61.6 tok/s
See what else this hardware can run →
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 |
BitNet b1.58 on MacBook Pro 16" (M4 Max, 128 GB): memory by quantization
| Quant | Memory needed | Fits 96 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 8.3 GB | ✓ Yes | — | ~37.5 tok/s | 6.6 GB |
| Q8_0 | 5.2 GB | ✓ Yes | — | ~61.6 tok/s | 3.5 GB |
| Q6_K | 4.4 GB | ✓ Yes | — | ~73.8 tok/s | 2.7 GB |
| Q5_K_M | 4 GB | ✓ Yes | — | ~81.2 tok/s | 2.4 GB |
| Q4_K_M | 3.7 GB | ✓ Yes | — | ~89.6 tok/s | 2 GB |
| Q3_K_M | 3.1 GB | ✓ Yes | — | ~108.9 tok/s | 1.4 GB |
| Q2_K | 2.8 GB | ✓ Yes | — | ~123.4 tok/s | 1.1 GB |
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.
FAQ
Can I run BitNet b1.58 on MacBook Pro 16" (M4 Max, 128 GB)?
Yes, comfortably — BitNet b1.58 3B at Q8_0 needs about 5.2 GB of the 96 GB usable on MacBook Pro 16" (M4 Max, 128 GB), leaving ~90.8 GB spare and running at ~61.6 tok/s (estimated).
Which quantization of BitNet b1.58 should I use on MacBook Pro 16" (M4 Max, 128 GB)?
Q8_0 — it needs about 5.2 GB of the 96 GB available, downloads as roughly 3.5 GB, and runs at an estimated 61.6 tokens/sec.
What limits BitNet b1.58 on MacBook Pro 16" (M4 Max, 128 GB)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput
Other Computers
- BitNet b1.58 on MacBook Pro M4 Max 48 GB
- BitNet b1.58 on MacBook Pro M4 Pro 24 GB
- BitNet b1.58 on MacBook Air M4 16 GB
Other Models on MacBook Pro 16" (M4 Max, 128 GB)
- Bonsai 27B on MacBook Pro 16" (M4 Max, 128 GB)
- Codestral on MacBook Pro 16" (M4 Max, 128 GB)
- Cogito v1 on MacBook Pro 16" (M4 Max, 128 GB)
- Command R Family on MacBook Pro 16" (M4 Max, 128 GB)
- Cosmos 3 on MacBook Pro 16" (M4 Max, 128 GB)
BitNet b1.58 on GPUs
- BitNet b1.58 on NVIDIA GeForce RTX 5060 Ti 8GB
- BitNet b1.58 on NVIDIA GeForce RTX 5060
- BitNet b1.58 on NVIDIA GeForce RTX 4060
What This Model Is Good At
Model & Tools
← Can I Run It? | BitNet b1.58 model page | Check your hardware