Can I Run OLMo 2 on MacBook Pro 16" (M4 Max, 128 GB)?
Written by Jakub Rusinowski · Last updated November 26, 2024
Yes — comfortably
Yes, comfortably — OLMo 2 13B Instruct at Q8_0 needs about 22.1 GB of the 96 GB usable on MacBook Pro 16" (M4 Max, 128 GB), leaving ~73.9 GB spare and running at ~15.9 tok/s (estimated), with room for about 4,096 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~15.9 tok/s
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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 |
OLMo 2 on MacBook Pro 16" (M4 Max, 128 GB): memory by quantization
| Quant | Memory needed | Fits 96 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 34.9 GB | ✓ Yes | 4K | ~9.5 tok/s | 27.4 GB |
| Q8_0 | 22.1 GB | ✓ Yes | 4K | ~15.9 tok/s | 14.6 GB |
| Q6_K | 18.7 GB | ✓ Yes | 4K | ~19.3 tok/s | 11.2 GB |
| Q5_K_M | 17.2 GB | ✓ Yes | 4K | ~21.4 tok/s | 9.7 GB |
| Q4_K_M | 15.8 GB | ✓ Yes | 4K | ~23.9 tok/s | 8.3 GB |
| Q3_K_M | 13.4 GB | ✓ Yes | 4K | ~29.7 tok/s | 5.8 GB |
| Q2_K | 12 GB | ✓ Yes | 4K | ~34.2 tok/s | 4.5 GB |
Which OLMo 2 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| OLMo 2 13B Instruct | 15.8 GB | ✓ Fits | ~23.9 tok/s |
| OLMo 2 7B Instruct | 9.5 GB | ✓ Fits | ~40.2 tok/s |
What to watch out for
- Context is capped at about 4,096 tokens before memory runs out, which is short for document or agent work.
- 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 computed from this model's published attention configuration.
FAQ
Can I run OLMo 2 on MacBook Pro 16" (M4 Max, 128 GB)?
Yes, comfortably — OLMo 2 13B Instruct at Q8_0 needs about 22.1 GB of the 96 GB usable on MacBook Pro 16" (M4 Max, 128 GB), leaving ~73.9 GB spare and running at ~15.9 tok/s (estimated), with room for about 4,096 tokens of context.
Which quantization of OLMo 2 should I use on MacBook Pro 16" (M4 Max, 128 GB)?
Q8_0 — it needs about 22.1 GB of the 96 GB available, downloads as roughly 14.6 GB, and runs at an estimated 15.9 tokens/sec with up to 4K of context.
What limits OLMo 2 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
Other Models on MacBook Pro 16" (M4 Max, 128 GB)
- Phi 3.5 Family on MacBook Pro 16" (M4 Max, 128 GB)
- Phi-4 Family on MacBook Pro 16" (M4 Max, 128 GB)
- Phi-4 Mini on MacBook Pro 16" (M4 Max, 128 GB)
- Poolside Laguna XS 2.1 on MacBook Pro 16" (M4 Max, 128 GB)
- Qwen 2.5 Family on MacBook Pro 16" (M4 Max, 128 GB)
OLMo 2 on GPUs
- OLMo 2 on NVIDIA GeForce RTX 5080
- OLMo 2 on NVIDIA GeForce RTX 5070 Ti
- OLMo 2 on NVIDIA GeForce RTX 5060 Ti 16GB
- OLMo 2 on NVIDIA GeForce RTX 5060 Ti 8GB