Run LFM2.5 1.2B on a Mac
Liquid's hybrid conv+attention architecture keeps the KV cache tiny, so long chats don't balloon memory the way same-size transformers do. At 1.1 GB resident it runs on literally any Apple Silicon Mac.
1.2 B hybrid conv+attention · 4-bit 8 GB Macs · long chats on small memory · quick Q&A
One command
$ rapid-mlx serve lfm2.5-1b-4bit
Weights (0.6 GB) download on first run; you get an OpenAI-compatible
endpoint at http://localhost:8000/v1. No rapid-mlx yet? It's one line —
curl -fsSL https://rapidmlx.com/install.sh | bash — or take the
desktop app.
Measured on real hardware
| Machine | Decode | First token | Peak memory | Cold boot | Weights |
|---|---|---|---|---|---|
| Mac mini M2 Pro · 32 GB | 209.8 tok/s | 0.2 s | 1.1 GB | 3.2 s | 0.6 GB |
Measured on rapid-mlx 0.12.10, 2026-08-11. Decode: median of 3 runs, temperature 0, 256-token saturating generation, engine-reported token counts, unique salt per request (no prefix-cache hits). TTFT: median of 3, short prompt. Boot: process spawn to first completed token. Peak RSS: 0.5 s sampling across the run.
Will it fit your Mac?
Peak resident memory measured 1.1 GB during a 256-token generation. The KV cache grows with context length, so treat that as a floor, not a ceiling. For the conservative install-default placement see the hardware tiers table; to compare against every model your RAM can hold, use the live picker.
Variants & alternatives
lfm2.5-2.6b-4bit— LFM2.5 2.6B — same architecture, noticeably smarter
FAQ
How much memory does LFM2.5 1.2B need on a Mac?
Measured peak resident memory was 1.1 GB on rapid-mlx 0.12.10 during a 256-token generation (M2 Pro, 32 GB). The weights are 0.6 GB on disk. Longer contexts grow the KV cache beyond this, so leave headroom.
How fast is LFM2.5 1.2B on Apple Silicon?
We measured 209.8 tokens/sec sustained decode and 0.2 s time-to-first-token on a Mac mini M2 Pro (32 GB), median of 3 runs at temperature 0.
How do I run LFM2.5 1.2B locally?
Install rapid-mlx (curl -fsSL https://rapidmlx.com/install.sh | bash, or brew install rapid-mlx), then: rapid-mlx serve lfm2.5-1b-4bit — the weights download on first run and you get an OpenAI-compatible endpoint at localhost:8000/v1 that works with Cursor, Claude Code, Aider, and any OpenAI client.