Liquid AI
LFM2.5 8B A1B
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116.1KDownloads658LikesMay 2026Veröffentlicht128K TokenKontextOtherLizenz58 GutQualität
LFM2.5 8B A1B (8.5B parameters) requires approximately 6.9 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 1.5B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 8 GB of VRAM.
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— kopieren & einfügen, um lokal auszuführenCopy-paste commands to run LFM2.5 8B A1B on your machine.
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lms load LFM2.5-8B-A1B && lms server startQuick specs
Parameters8.5B (1.5B active)
Architecturemoe (MoE)
Context128K tokens
Modalitytext
Min RAM3.3 GB
Rec. RAM5.2 GB (Q4_K_M)
LicenseOther
FamilyLFM2
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About this model
- •Only ~1.5B active parameters per token — MoE efficiency for on-device use.
- •Hybrid short-convolution + grouped-query-attention backbone (LFM2 architecture).
- •128K context, multilingual (en, ar, zh, fr, de, ja, ko).
- •GGUF and MLX builds recommended for llama.cpp and Apple Silicon.
Verwandte Modelle
Schnellauswahl
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Dieses Modell ausführen
Quantisierungsoptionen
VRAM-Schätzungen nach Quantisierungsstufe
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.3 GB | Low | — |
Q3_K_S | 3 | 4.2 GB | Low | — |
NVFP4 | 4 | 4.8 GB | Medium | — |
Q4_K_M | 4 | 5.2 GB | Medium | — |
Q5_K_M | 5 | 6.1 GB | High | — |
Q6_K | 6 | 7.0 GB | High | — |
Q8_0 | 8 | 9.1 GB | Very High | — |
F16 | 16 | 17.4 GB | Maximum | — |
Hardware-Kompatibilität
Eignungsschätzungen für alle Hardware
Computing compatibility...
Speicheraufschlüsselung
Reference: RTX 2060 6GB
Weights5.2 GB
KV Cache0.2 GB
Runtime0.9 GB
Headroom0.6 GB
Häufig gestellte Fragen
FAQ — LFM2.5 8B A1B
Siehe auch