Decision models · Alibaba

GTE Reranker ModernBERT Base

Name: GTE Reranker ModernBERT Base
Author: Alibaba

Task: Reranking search results
Returns: Probability or score
Precision tier: 70 / 100
Size: 150M
Max input: 8K tokens
License: apache-2.0
Released: 2025-01

What it decides

Returns: A relevance score for each query and document pair.
Checks: Long query and document pairs (8K)
Languages: English
Precision: 70 / 100 · Small, fast English reranker with an 8K window.
Clarity: Probability or score (15 of 15 clarity points)
How it runs: Scores query and document pairs · reads the input once and writes nothing, so it answers in milliseconds.
License: apache-2.0 (Open (Apache, MIT))

Source: model card on Hugging Face (opens in a new tab) · How decision models are rated

How to run it

Original weights · Hugging Face

Full-precision files from Alibaba, for Transformers, Sentence Transformers, Text Embeddings Inference, Infinity or vLLM.

huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

Apps that run GTE Reranker ModernBERT Base

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or Mac in the Model Finder for an exact answer.

Mac (Apple silicon)

Text Embeddings Inference

Purpose-built server for encoder rerankers and classifiers: batching and Flash Attention keep each call to milliseconds.

Also good: Infinity, Sentence Transformers

Windows PC, NVIDIA GPU

Text Embeddings Inference

Purpose-built server for encoder rerankers and classifiers: batching and Flash Attention keep each call to milliseconds.

Also good: Sentence Transformers, Infinity

Windows PC, AMD Radeon

Sentence Transformers

A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks.

Also good: Infinity, vLLM

Linux, NVIDIA GPU

Text Embeddings Inference

Purpose-built server for encoder rerankers and classifiers: batching and Flash Attention keep each call to milliseconds.

Also good: Infinity, vLLM

Linux server, 80 GB+ GPUs

Text Embeddings Inference

Purpose-built server for encoder rerankers and classifiers: batching and Flash Attention keep each call to milliseconds.

Also good: vLLM, Infinity

App	Type	Opens it as	Systems	Engine	Start
vLLM	Server	Original weights	WSLWindowsLinux	vLLM	`vllm serve Alibaba-NLP/gte-reranker-modernbert-base`
Text Embeddings Inference	Server	Original weights	macOSWSLWindowsLinux	TEI	`text-embeddings-router --model-id Alibaba-NLP/gte-reranker-modernbert-base --port 8080`
Infinity	Server	Original weights	macOSWSLWindowsLinux	PyTorch, ONNX, CTranslate2	`infinity_emb v2 --model-id Alibaba-NLP/gte-reranker-modernbert-base`
LocalAI	Server	Original weights	macOSWSLWindowsLinux	llama.cpp, rerankers, Transformers	Get LocalAI
Sentence Transformers	Library	Original weights	macOSWindowsLinux	PyTorch	`python -c "from sentence_transformers import CrossEncoder; print(CrossEncoder('Alibaba-NLP/gte-reranker-modernbert-base').predict([('query', 'document')]))"`

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory and speed

Each precision on an NVIDIA GeForce RTX 4090, for one decision on a 512-token input. The best hardware table below covers other GPUs and Macs.

Format	Memory needed	Time per decision	Decisions per second	Quality	Download
`Q8_0`	0.6 GB	5.4 ms	185/s	70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	0.7 GB	5.6 ms	178/s	70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

One request at a time. Servers that batch (Text Embeddings Inference, vLLM, Infinity) handle many more decisions per second.

Best hardware for GTE Reranker ModernBERT Base

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc A380 (launch price $139) at F16.

Quality	Hardware	Quant	Memory	Time per decision	Runs
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 7900 XTX	`F16`	0.9 GB / 24.0 GB	5.3 ms	Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 9070 XT	`F16`	0.9 GB / 16.0 GB	5.6 ms	Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	AMD Ryzen AI Max+ 395 (128 GB)	`F16`	0.9 GB / 96.0 GB	6.6 ms	Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Apple M1 (16 GB)	`F16`	0.9 GB / 10.7 GB	64 ms	Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Apple M1 Max (64 GB)	`F16`	0.9 GB / 48.0 GB	19 ms	Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Apple M2 Ultra (192 GB)	`F16`	0.9 GB / 144 GB	9.7 ms	Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Apple M3 Ultra (256 GB)	`F16`	0.9 GB / 192 GB	9.5 ms	Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Apple M3 Ultra (512 GB)	`F16`	0.9 GB / 384 GB	9.5 ms	Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 (16 GB)	`F16`	0.9 GB / 10.7 GB	40 ms	Runs great
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Apple M4 (24 GB)	`F16`	0.9 GB / 16.1 GB	40 ms	Runs great

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.