GTE ModernBERT Base
GTE ModernBERT Base is a 149M-parameter English embedder built on AnswerDotAI's ModernBERT backbone, producing 768-dim vectors with native 8192-token context via alternating local/global attention. It pairs ModernBERT's modern architecture (RoPE, GeGLU, unpadding) with Alibaba's GTE retrieval pretraining, yielding an MTEB score of ~64 at less than half the parameters of BGE-large.
The most architecturally interesting English embedder of 2025 — ModernBERT's design choices (RoPE + unpadding + alternating attention) translate directly into throughput wins at no quality cost. The clear successor to nomic-v1.5 for English-only edge deployments. Skip if you need Matryoshka truncation or multilingual coverage.
Overview
GTE ModernBERT Base is a 149M-parameter English embedder built on AnswerDotAI's ModernBERT backbone, producing 768-dim vectors with native 8192-token context via alternating local/global attention. It pairs ModernBERT's modern architecture (RoPE, GeGLU, unpadding) with Alibaba's GTE retrieval pretraining, yielding an MTEB score of ~64 at less than half the parameters of BGE-large.
Strengths
- 149M params hitting MTEB ~64 — best quality-per-param in the English embedder tier
- ModernBERT alternating local/global attention gives true 8K context at near-flash-attention speeds
- Apache-2.0 with documented training mix — production-safe
- Unpadded inference via Flash Attention 2 — 2-4x faster than BERT-base at the same batch size
Weaknesses
- 768-dim only, no Matryoshka truncation support
- English-only — no multilingual ModernBERT-GTE variant released
- Requires modernbert-compatible transformers (>=4.48) and ideally Flash Attention 2 for full speedup
- Newer architecture means fewer downstream fine-tunes and ecosystem tools than BGE/E5
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 0.1 GB | 1 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of GTE ModernBERT Base.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run GTE ModernBERT Base?
Can I use GTE ModernBERT Base commercially?
What's the context length of GTE ModernBERT Base?
Source: huggingface.co/Alibaba-NLP/gte-modernbert-base
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify GTE ModernBERT Base runs on your specific hardware before committing money.