Can I Run LFM2.5 on MacBook Pro 14" (M4 Pro, 24 GB)?
Written by Jakub Rusinowski · Last updated October 15, 2026
Yes, but it is tight
Yes, but it is tight — LFM2.5-24B-A2B at Q5_K_M needs about 18 GB of the 18 GB usable on MacBook Pro 14" (M4 Pro, 24 GB), leaving only ~0 GB before the runtime starts swapping. Expect ~65.9 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q5_K_M · Estimated speed: ~65.9 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) |
LFM2.5 on MacBook Pro 14" (M4 Pro, 24 GB): memory by quantization
| Quant | Memory needed | Fits 18 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 48.9 GB | ✗ No | — | — | 48 GB |
| Q8_0 | 26.5 GB | ✗ No | — | — | 25.5 GB |
| Q6_K | 20.7 GB | ✗ No | — | — | 19.7 GB |
| Q5_K_M | 18 GB | ✓ Yes | 8K | ~65.9 tok/s | 17 GB |
| Q4_K_M | 15.5 GB | ✓ Yes | 64K | ~74.4 tok/s | 14.5 GB |
| Q3_K_M | 11.2 GB | ✓ Yes | 128K | ~94.9 tok/s | 10.2 GB |
| Q2_K | 8.8 GB | ✓ Yes | 128K | ~111.8 tok/s | 7.9 GB |
Which LFM2.5 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| LFM2.5-24B-A2B | 15.5 GB | ✓ Fits | ~74.4 tok/s |
| LFM2.5-8B-A1B | 7 GB | ✓ Fits | ~77.1 tok/s |
| LFM2.5 2.6B | 3.2 GB | ✓ Fits | ~60.4 tok/s |
What to watch out for
- Only ~0 GB of headroom at Q5_K_M: a longer context or a second application can push this into swapping.
- 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 computed from this model's published attention configuration.
FAQ
Can I run LFM2.5 on MacBook Pro 14" (M4 Pro, 24 GB)?
Yes, but it is tight — LFM2.5-24B-A2B at Q5_K_M needs about 18 GB of the 18 GB usable on MacBook Pro 14" (M4 Pro, 24 GB), leaving only ~0 GB before the runtime starts swapping. Expect ~65.9 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of LFM2.5 should I use on MacBook Pro 14" (M4 Pro, 24 GB)?
Q5_K_M — it needs about 18 GB of the 18 GB available, downloads as roughly 17 GB, and runs at an estimated 65.9 tokens/sec with up to 8K of context.
What limits LFM2.5 on MacBook Pro 14" (M4 Pro, 24 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 14" (M4 Pro, 24 GB)
- Llama 3.1 Family on MacBook Pro 14" (M4 Pro, 24 GB)
- Llama 3.2 Family on MacBook Pro 14" (M4 Pro, 24 GB)
- Llama 3.2 Vision on MacBook Pro 14" (M4 Pro, 24 GB)
- Magistral Small on MacBook Pro 14" (M4 Pro, 24 GB)
- MiniCPM-V on MacBook Pro 14" (M4 Pro, 24 GB)
LFM2.5 on GPUs
- LFM2.5 on NVIDIA GeForce RTX 5070
- LFM2.5 on NVIDIA GeForce RTX 5060 Ti 8GB
- LFM2.5 on NVIDIA GeForce RTX 5060
- LFM2.5 on NVIDIA GeForce RTX 4070 Ti