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text model · LFM · macOS

Can I run LFM2.5 1.2B Thinking on Apple M4 Pro (48GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs GPU accelerated ~321 tok/s est.

Yes. LFM2.5 1.2B Thinking runs on Apple M4 Pro (48GB) at Q4_K_M (~1.8 GB of ~32 GB usable).

Needs ~1.8 GB Device usable ~32 GB

Runs at Q4_K_M using ~1.8 GB of ~32 GB usable. You have room for FP16 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Pro (48GB) leaves ~30.2 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed
~1.8 GB
Usable on device
~32 GB
Device memory
48 GB
Best quant
Q4_K_M
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Which quant fits

Quant ladder vs ~32 GB usable
Q2_K
~1.6 GB
Q3_K_M
~1.7 GB
Q4_K_M
~1.8 GB
Q5_K_M
~1.9 GB
Q6_K
~2.1 GB
Q8_0
~2.3 GB
FP16
~3.3 GB
The line marks Apple M4 Pro (48GB)'s ~32 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.
Power draw
~140 W
Electricity / 1M tokens
~$0.02
Pays for itself after
~4,998M tok

At ~$0.15/kWh and the estimated ~321 tok/s, a million generated tokens costs about $0.02 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$2,399 Apple M4 Pro (48GB) pays for itself after roughly 4,998 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands macOS

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama
$ ollama run lfm2.5-thinking:1.2b
llama.cpp
$ llama-cli -hf LiquidAI/LFM2.5-1.2B-Thinking-GGUF:Q4_K_M
LM Studio
$ lms get LiquidAI/LFM2.5-1.2B-Thinking-GGUF
Model LFM
Parameters
1.17B
Q4_K_M size
0.68 GB
Q8_0 size
1.16 GB
Context
128k
Ollama tag
lfm2.5-thinking:1.2b
Full LFM2.5 1.2B Thinking requirements →
Device macOS
Memory
48 GB unified
Usable for weights
~32 GB
Power draw
~140 W
Best runtime
Ollama (MLX backend) / MLX direct
Best models for Apple M4 Pro (48GB) →

You could also run

Run LFM2.5 1.2B Thinking on other hardware

Apple M1 (8GB)8GB RAM Laptop (CPU/iGPU only)iPhone 15 ProiPhone 16iPhone 16 ProGeneric Android Phone (8GB RAM)

FAQ

Can Apple M4 Pro (48GB) run LFM2.5 1.2B Thinking?

Yes. LFM2.5 1.2B Thinking runs on Apple M4 Pro (48GB) at Q4_K_M (~1.8 GB of ~32 GB usable).

How much memory does LFM2.5 1.2B Thinking need?

Apple M4 Pro (48GB) has room to spare. At Q4_K_M the weights are ~0.68 GB; with KV cache and runtime overhead, budget ~1.8 GB at a 4k context.

What is the best tool to run LFM2.5 1.2B Thinking on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

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Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.