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Can the NVIDIA Jetson AGX Thor (128 GB) run RecurrentGemma 9B IT?

32Basic quality (D) · 32 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q4_K_M it needs about 15.8 GB of 115 GB usable, with an estimated speed of 34.8 tok/s.

Best quant
Q4_K_M
Generation speed
34.8 tok/s
Prompt processing
598 tok/s
First token (1K prompt)
1.7 s
Largest safe context
8K

Download RecurrentGemma 9B IT

Original weights · Hugging Face

Full-precision files from Google, for fine-tuning, vLLM, Transformers.

huggingface.co/google/recurrentgemma-9b-it (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Google approves it (usually within minutes to a few hours).

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 32Basic quality (D) · 32 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, 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.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.14.5 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.14.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.3.2 / 15
Match score56.7 / 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
Q2_K 13.8 GB 115 GB 57.2 tok/s Runs great 23Basic quality (D) · 23 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 14.6 GB 115 GB 45.0 tok/s Runs great 28Basic quality (D) · 28 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 15.8 GB 115 GB 34.8 tok/s Runs great 32Basic quality (D) · 32 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 16.8 GB 115 GB 28.7 tok/s Runs well 32Basic quality (D) · 32 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 17.8 GB 115 GB 24.1 tok/s Runs well 33Basic quality (D) · 33 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 20.1 GB 115 GB 17.7 tok/s Runs well 34Basic quality (D) · 34 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 29.2 GB 115 GB 8.2 tok/s Slow 34Basic quality (D) · 34 of 100: how capable the model is at F16, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does RecurrentGemma 9B IT need on the NVIDIA Jetson AGX Thor 128GB?

At Q4_K_M it needs about 15.8 GB. The NVIDIA Jetson AGX Thor 128GB has 115 GB usable.

How fast is RecurrentGemma 9B IT on the NVIDIA Jetson AGX Thor 128GB?

About 34.8 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of RecurrentGemma 9B IT should I use on the NVIDIA Jetson AGX Thor 128GB?

Q4_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.

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