Model reference · open weights

LFM3-VL

LLMs LiquidAI Vision + text 2 builds Its own licence terms 6k dl/mo

LFM3-VL is an open-weight language model from LiquidAI. LFM3-VL-2B-GGUF (GGUF) weighs 1.1 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • LFM3-VL is a 2B parameter image-text-to-text model developed by LiquidAI.
  • It is distributed as a GGUF file for local inference on CPUs and GPUs, with the Q4_0 build using Quantization-Aware Distillation to maintain accuracy.
  • The model supports languages including English, Arabic, Chinese, French, German, Italian, Japanese, and Korean.

Summary of the LiquidAI/LFM3-VL-2B-GGUF model card, 2026-10-29

What it is

Released byLiquidAI
Released2026-10-19
VRAM1.1 GB for the weights

What it runs on

Memory and cards for LFM3-VL-2B-GGUF (GGUF)

1.1 GBweights, file size
651 MBruntime overhead, at least

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GBfits
RTX 4060 Ti 16 GBfits
RTX 3090 24 GBfits
RTX 4090 24 GBfits
RTX 5090 32 GBfits
L40S 48 GBfits
A100 80 GBfits
H100 80 GBfits
RTX PRO 6000 Blackwell 96 GBfits
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
LFM3-VL-2B-GGUF (above) ↗
7 builds: Q4_0 1.0 GB … F16 3.5 GB
GGUF 1.1 GBRTX 3060 12 GB
LFM3-VL-2B ↗ 1.9BBF16 3.8 GBRTX 3060 12 GB

From the model card

What LiquidAI says about LFM3-VL

Read the model card

src="https://huggingface.co/LiquidAI/LFM3-1.7B/resolve/main/assets/liquid-ai-banner.webp" alt="Liquid AI" style="width: 100%; max-width: 100%; height: auto; display: block; margin: 0;" />

LFM3-VL-2B-GGUF

GGUF version of LFM3-VL-2B for local inference on CPUs and GPUs.

Find architecture and usage details in the original model card.

How to run LFM3-VL-2B

Recommended sampling: temperature 0.1, top_k 50, top_p 1.0, min_p 0.0, repeat penalty 1.1.

  • Llama.app (Use this model > Llama in the menu), for macOS and Windows: image and video input. Requires llama.cpp version v0.8.0 or newer.
  • LM Studio (Use this model > LM Studio in the menu), for macOS, Windows and Linux: image input. Requires LM Studio with llama.cpp engine 2.65.1 or newer.
  • Ollama ollama run lfm3-vl, for macOS: image input. Requires Ollama version v0.40.12 or newer.

QAD Q4_0 GGUF

The Q4_0 build in this repository is the Quantization-Aware Distillation (QAD) checkpoint, LFM3-VL-2B-QAD-Q4_0.gguf. It was trained to recover the accuracy lost to Q4_0 quantization.

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.
© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms