Liquid AI
LFM2 24B (24B parameters) requires approximately 18.9 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 22 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run LFM2 24B on your machine.
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ollama run lfm2Quick specs
About this model
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Quantization options
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | — |
Q3_K_S | 3 | 11.8 GB | Low | — |
NVFP4 | 4 | 13.4 GB | Medium | — |
Q4_K_M | 4 | 14.6 GB | Medium | — |
Q5_K_M | 5 | 17.3 GB | High | — |
Q6_K | 6 | 19.7 GB | High | — |
Q8_0 | 8 | 25.7 GB | Very High | — |
F16 | 16 | 49.2 GB | Maximum | — |
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
LFM2 24B (24B parameters) requires approximately 18.9 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc Pro B60 24GB can run LFM2 24B with a compatibility score of 82/100. It provides 24 GB of memory and achieves approximately 18.1 tokens per second.
The recommended quantization for LFM2 24B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for LFM2 24B: RTX 5090 32GB (score: 89/100), RTX PRO 4500 Blackwell 32GB (score: 88/100), AMD Instinct MI100 32GB (score: 88/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, LFM2 24B is well-suited for chat as well as reasoning, coding. It was designed with these use cases in mind.
See also
