GTE Reranker ModernBERT Base on NVIDIA Jetson AGX Thor (128 GB)

Can the NVIDIA Jetson AGX Thor (128 GB) run GTE Reranker ModernBERT Base?

Yes, it runs great

Runs great. At F16 it needs about 0.9 GB of 115 GB usable and makes one decision in about 21 ms.

Best precision: F16
Time per decision: 21 ms
Decisions per second: 48/s
Input assumed: 512 tokens
Returns: Probability or score

Best app to run it on the NVIDIA Jetson AGX Thor (128 GB)

On Linux on ARM boards

vLLM

Inference server · runs on vLLM

Serves the scoring head directly (rerank, classify or reward) with batching.

vllm serve Alibaba-NLP/gte-reranker-modernbert-base

Infinity Local API for rerankers and classifiers, including models TEI does not support.
Sentence Transformers A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download GTE Reranker ModernBERT Base

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 2 hours ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 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.: How capable the model itself is at F16, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great: How well it runs on the NVIDIA Jetson AGX Thor (128 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

PrecisionEditorial precision tier from the model’s published benchmarks, scaled by the quantization or precision used.	31.5 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.	25.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.	15.0 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.	15.0 / 15
Match score	86.5 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the NVIDIA Jetson AGX Thor (128 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q8_0`	0.8 GB	115 GB	18 ms	Runs great	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`	0.9 GB	115 GB	21 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.	Original weights (opens in a new tab)

Times are for one request with a 512-token input. Batching servers process many requests at once.

Other hardware that runs it

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 · 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 · 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 · 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 · 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 · 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 · 9.7 ms · Runs great

Quick answers

How much memory does GTE Reranker ModernBERT Base need on the NVIDIA Jetson AGX Thor 128GB?

At F16 it needs about 0.9 GB. The NVIDIA Jetson AGX Thor 128GB has 115 GB usable.

How fast is GTE Reranker ModernBERT Base on the NVIDIA Jetson AGX Thor 128GB?

It makes one decision in about 21 ms at F16, an estimate from the hardware and the size of the model.

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.