LFM2.5 — local AI model by Liquid AI
Written by Jakub Rusinowski · Last updated
Liquid AI's on-device line, built around their hybrid LIV convolution architecture rather than a stack of attention blocks. The 8B-A1B is the flagship: a sparse MoE small enough for phones, laptops and robots, reasoning-only (it emits an explicit chain of thought before answering), and trained on 38T tokens with large-scale RL. Liquid claims tool-calling quality comparable to models up to 4x its active size.
Variants
The smallest LFM2.5 variant needs about 2 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache.
| Model | VRAM at Q4 | VRAM | Context | Run it |
|---|---|---|---|---|
| LFM2.5 2.6B → 2.69B | ~2.4 GB | 131,072 | ollama run lfm2-5 | |
| LFM2.5-8B-A1B → 8.3B (1.5B active) | ~5.8 GB | 131,072 | ollama run lfm2.5:8b | |
| LFM2.5-24B-A2B → 24B (2.45B active) | ~15.3 GB | 131,072 | ollama run lfm2.5:24b |
Memory is quantized weights plus overhead at Q4_K_M, from the same engine as the GPU & VRAM checker.
How to run LFM2.5 locally
Install Ollama, then pull the tag.
ollama run lfm2-5Pick a size above for its own VRAM figure, speed estimate and install command.
Licence
Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.
Applies to: LFM2.5 2.6B, LFM2.5-8B-A1B, LFM2.5-24B-A2BRecommended GPU
The cheapest catalogued GPU that runs LFM2.5 locally (min 2 GB VRAM) is the Intel Arc B570 (10 GB).
Can I run LFM2.5 on my GPU?
- LFM2.5 on AMD Radeon RX 9060 XT 8GB
- LFM2.5 on Intel Arc B570
- LFM2.5 on Intel Arc B580
- LFM2.5 on NVIDIA GeForce RTX 3060 (12GB)
- LFM2.5 on NVIDIA GeForce RTX 3070
- LFM2.5 on NVIDIA GeForce RTX 3070 Ti
- LFM2.5 on NVIDIA GeForce RTX 3080 (10GB)
- LFM2.5 on NVIDIA GeForce RTX 4060
- LFM2.5 on NVIDIA GeForce RTX 4070
- LFM2.5 on NVIDIA GeForce RTX 4070 Super
- LFM2.5 on NVIDIA GeForce RTX 4070 Ti
- LFM2.5 on NVIDIA GeForce RTX 5060
- LFM2.5 on NVIDIA GeForce RTX 5060 Ti 8GB
- LFM2.5 on NVIDIA GeForce RTX 5070
LFM2.5 — frequently asked questions
How much VRAM does LFM2.5 need?
LFM2.5 needs about 2 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: LFM2.5 2.6B (2 GB, Q4_K_M); LFM2.5-8B-A1B (6 GB, Q4_K_M); LFM2.5-24B-A2B (15 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run LFM2.5 on an RTX 4090 (24 GB)?
Yes — LFM2.5 runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
What quantization should I use for LFM2.5?
Q4_K_M is the best balance of quality and VRAM for LFM2.5 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run LFM2.5 with Ollama?
LFM2.5 has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.