Model reference · open weights
fasttext is an open-weight embedding model from NeuML. fasttext (FP32) weighs 600 MB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the NeuML/fasttext model card, 2026-10-01
What it is
| Released by | NeuML |
|---|---|
| Released | 2025-01-26 |
| Parameters | 300M |
| VRAM | 600 MB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
This model is an export of these FastText English Vectors (wiki-news-300d-1M-subword.vec.zip) for staticvectors. staticvectors enables running inference in Python with NumPy. This helps it maintain solid runtime performance.
from staticvectors import StaticVectors
model = StaticVectors("neuml/fasttext")
model.embeddings(["word"])
Given that pre-trained embeddings models can get quite large, there is also a SQLite version that lazily loads vectors.
from staticvectors import StaticVectors
model = StaticVectors("neuml/fasttext/model.sqlite")
model.embeddings(["word"])
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
How it works