LFM2.5-24B-A2B: hardware requirements
LFM2.5-24B-A2B needs about 16 GB of memory at Q4 quantization — a 15 GB download plus room for context and overhead. That fits a machine with 24GB RAM.
This is a mixture-of-experts model. Only a fraction of its 24B parameters are used for each token, so it runs faster than its size suggests — but all of it still has to fit in memory. The speed is that of a small model; the memory requirement is that of a large one. More on this →
Will it run on your machine?
Download sizes by quantization
Lower quantization means a smaller file and less memory, at some cost to quality. Q4_K_M is the usual starting point. What is this?
| Quant | Download | RAM @ 4K | RAM @ 32K | Source |
|---|---|---|---|---|
| Q4_K_Mrecommended | 15 GB | 16 GB | 19 GB | measured |
| Q5_K_M | 17 GB | 19 GB | 21 GB | measured |
| Q6_K | 20 GB | 22 GB | 24 GB | measured |
| Q8_0 | 25 GB | 28 GB | 31 GB | measured |
| BF16 | 48 GB | 52 GB | 54 GB | measured |
“Measured” sizes are read from published GGUF files (LiquidAI/LFM2.5-24B-A2B-GGUF). “Estimated” sizes are derived from the parameter count and are typically within a few percent. The RAM columns add the context cache and runtime overhead to the download size.