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text model · LFM · macOS

Can I run LFM3-1.7B on Apple M3 Ultra (256GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs GPU accelerated ~642 tok/s est.

Yes. LFM3-1.7B runs on Apple M3 Ultra (256GB) at Q4_K_M (~2.1 GB of ~192 GB usable).

Needs ~2.1 GB Device usable ~192 GB

Runs at Q4_K_M using ~2.1 GB of ~192 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 M3 Ultra (256GB) leaves ~189.9 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed
~2.1 GB
Usable on device
~192 GB
Device memory
256 GB
Best quant
Q4_K_M
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Which quant fits

Quant ladder vs ~192 GB usable
Q2_K
~1.8 GB
Q3_K_M
~1.9 GB
Q4_K_M
~2.1 GB
Q5_K_M
~2.3 GB
Q6_K
~2.5 GB
Q8_0
~2.8 GB
FP16
~4.3 GB
The line marks Apple M3 Ultra (256GB)'s ~192 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.
Power draw
~270 W
Electricity / 1M tokens
~$0.02
Pays for itself after
~8,331M tok

At ~$0.15/kWh and the estimated ~642 tok/s, a million generated tokens costs about $0.02 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$3,999 Apple M3 Ultra (256GB) pays for itself after roughly 8,331 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 lfm3:1.7b
llama.cpp
$ llama-cli -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M
LM Studio
$ lms get LiquidAI/LFM3-1.7B-GGUF
Model LFM
Parameters
1.73B
Q4_K_M size
1.02 GB
Q8_0 size
1.72 GB
Context
256k
Ollama tag
lfm3:1.7b

Set up this model at 4K →

Full LFM3-1.7B requirements →
Device macOS
Memory
256 GB unified
Usable for weights
~192 GB
Power draw
~270 W
Best runtime
MLX direct / Ollama (MLX backend)
Best models for Apple M3 Ultra (256GB) →

You could also run

Run LFM3-1.7B on other hardware

Nvidia GeForce RTX 2060 (6GB)Apple M1 (8GB)8GB RAM Laptop (CPU/iGPU only)iPhone 15 ProiPhone 16iPhone 16 Pro

FAQ

Can Apple M3 Ultra (256GB) run LFM3-1.7B?

Yes. LFM3-1.7B runs on Apple M3 Ultra (256GB) at Q4_K_M (~2.1 GB of ~192 GB usable).

How much memory does LFM3-1.7B need?

Apple M3 Ultra (256GB) has room to spare. At Q4_K_M the weights are ~1.02 GB; with KV cache and runtime overhead, budget ~2.1 GB at a 4k context.

What is the best tool to run LFM3-1.7B 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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LFM3-1.7B on Apple M3 Ultra (256GB) compatibility badge A live badge for your model card or README, updated as the data is.
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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.