Skip to content
localmodel.run

text model · LFM · macOS

Can I run LFM3 Omni 2B on Apple M1 (8GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs GPU accelerated ~29 tok/s est.

Yes. LFM3 Omni 2B runs on Apple M1 (8GB) at Q4_K_M (~3.1 GB of ~5.5 GB usable).

Needs ~3.1 GB Device usable ~5.5 GB

Runs at Q4_K_M using ~3.1 GB of ~5.5 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 M1 (8GB) leaves ~2.4 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed
~3.1 GB
Usable on device
~5.5 GB
Device memory
8 GB
Best quant
Q4_K_M
Share on X Share on Reddit

Which quant fits

Quant ladder vs ~5.5 GB usable
Q2_K
~2.1 GB
Q3_K_M
~2.3 GB
Q4_K_M
~3.1 GB
Q5_K_M
~2.7 GB
Q6_K
~3 GB
Q8_0
~3.8 GB
FP16
~5.3 GB
The line marks Apple M1 (8GB)'s ~5.5 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.
Power draw
~39 W
Electricity / 1M tokens
~$0.06
Pays for itself after
~2,270M tok

At ~$0.15/kWh and the estimated ~29 tok/s, a million generated tokens costs about $0.06 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$999 Apple M1 (8GB) pays for itself after roughly 2,270 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-omni:2b
llama.cpp
$ llama-cli -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M
LM Studio
$ lms get LiquidAI/LFM3-Omni-2B-GGUF
Model LFM
Parameters
2.17B
Q4_K_M size
1.91 GB
Q8_0 size
2.6 GB
Context
125k
Ollama tag
lfm3-omni:2b
Full LFM3 Omni 2B requirements →
Device macOS
Memory
8 GB unified
Usable for weights
~5.5 GB
Power draw
~39 W
Best runtime
Ollama (llama.cpp Metal backend)
Best models for Apple M1 (8GB) →

You could also run

Run LFM3 Omni 2B on other hardware

Nvidia GeForce RTX 2060 (6GB)8GB RAM Laptop (CPU/iGPU only)iPhone 15 ProiPhone 16iPhone 16 ProGeneric Android Phone (8GB RAM)

FAQ

Can Apple M1 (8GB) run LFM3 Omni 2B?

Yes. LFM3 Omni 2B runs on Apple M1 (8GB) at Q4_K_M (~3.1 GB of ~5.5 GB usable).

How much memory does LFM3 Omni 2B need?

Apple M1 (8GB) has room to spare. At Q4_K_M the weights are ~1.91 GB; with KV cache and runtime overhead, budget ~3.1 GB at a 4k context.

What is the best tool to run LFM3 Omni 2B 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.

Embed this

LFM3 Omni 2B on Apple M1 (8GB) compatibility badge A live badge for your model card or README, updated as the data is.
Markdown
$ [![LFM3 Omni 2B on Apple M1 (8GB)](https://localmodel.run/badge/lfm3-omni-2b/apple-m1-8gb.svg)](https://localmodel.run/can-i-run/lfm3-omni-2b/apple-m1-8gb)

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.