Does GR00T N1.7 run on SO-100?

works

T1 · upstream documentedlast verified 2026-10-24 · 1d agoIsaac GR00T

Capabilities

Capability	State	Notes	Evidence
Train / fine-tune	works	Fine-tuning example uses an so101_follower dataset; SO-100 data follows the same NEW_EMBODIMENT recipe.	ev-isaac-groot-so100-example
Real-robot inference	works	Isaac-GR00T's SO100/SO101 eval script drives a LeRobot so100_follower or so101_follower; the worked example data is SO-101.	ev-isaac-groot-eval-so100
Simulation inference	no verified evidence		—
Teleop + record	no verified evidence		—

Evidence 2 sources; the cell shows the highest tier

T1 · upstream documentedsource-codesource 2026-04-18verified 2026-10-24 (1d ago)isaac-groot n1.7-release
Isaac-GR00T n1.7-release real-robot eval script (LeRobot RobotConfig + GR00T policy server) states it targets both SO100 and SO101; it imports LeRobot so100_follower and so101_follower configs.
“This script runs closed-loop policy evaluation on the SO100 / SO101 robots using the GR00T Policy API.”
https://github.com/NVIDIA/Isaac-GR00T/blob/n1.7-release/gr00t/eval/real_robot/SO100/eval_so100.py
T1 · upstream documentedofficial-docssource 2026-04-18verified 2026-10-24 (1d ago)isaac-groot n1.7-release
Isaac-GR00T n1.7-release 'SO100' example fine-tunes GR00T-N1.7-3B (NEW_EMBODIMENT) on izuluaga/finish_sandwich (meta/info.json robot_type=so101_follower) and runs closed-loop eval with --robot.type=so101_follower.
“This guide shows how to finetune dataset collected from [SO100](https://huggingface.co/docs/lerobot/en/so101) robot, and evaluate the model on the real robot.”
https://github.com/NVIDIA/Isaac-GR00T/blob/n1.7-release/examples/SO100/README.md

GR00T N1.7 inference speed by hardware robot-independent · inference cost only

Compute	Runtime	Precision	Cams	p50 / p95 ms	Inference	Platform	Source
H100 80GB	TensorRT	bf16	2 @256x256	22.9 / —	43.67 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
RTX PRO 6000 Blackwell Server	TensorRT	bf16	2 @256x256	24.7 / —	40.49 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
L40	TensorRT	bf16	2 @256x256	35 / —	28.57 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
Jetson AGX Thor 128GB	TensorRT	mixed	2 @256x256	35.6 / —	28.09 Hz	jetpack 7.2 · power_mode MAXN with jetson_clocks · cuda 13.2 · torch 2.13.0 conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Mixed NVFP4 quantization (enum has no fp4 value).	T3 · community reported
RTX PRO 6000 Blackwell Server	torch.compile	bf16	2 @256x256	52.1 / —	19.19 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
H100 80GB	torch.compile	bf16	2 @256x256	53.4 / —	18.73 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
L40	torch.compile	bf16	2 @256x256	66.8 / —	14.97 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
RTX PRO 6000 Blackwell Server	PyTorch eager	bf16	2 @256x256	75.7 / —	13.21 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
Jetson AGX Thor 128GB	TensorRT	bf16	2 @256x256	76.8 / —	13.02 Hz	jetpack 7.2 · power_mode MAXN with jetson_clocks · cuda 13.2 · torch 2.13.0 conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
Jetson AGX Thor 128GB	torch.compile	bf16	2 @256x256	100.5 / —	9.95 Hz	jetpack 7.2 · power_mode MAXN with jetson_clocks · cuda 13.2 · torch 2.13.0 conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
H100 80GB	PyTorch eager	bf16	2 @256x256	101.5 / —	9.85 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
Jetson AGX Thor 128GB	PyTorch eager	bf16	2 @256x256	106.8 / —	9.36 Hz	jetpack 7.2 · power_mode MAXN with jetson_clocks · cuda 13.2 · torch 2.13.0 conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
L40	PyTorch eager	bf16	2 @256x256	123.7 / —	8.08 Hz	— conditions torch_threads: 8 intra-op / 1 inter-op Inference cost only; not task success. Median of 60 timed calls (3 repetitions x 20 after 10 warmup), fixed inputs, includes preprocessing and action decoding, excludes RPC. Precision is the repo default (unquantized BF16).	T3 · community reported
Jetson Orin Nano 8GB (Super)	TensorRT	fp16	3 @640x480	273.7 / 275.4	3.66 Hz	jetpack 7.2.1 · power_mode MAXN_SUPER, clocks pinned with jetson_clocks (GPU 1020 MHz, CPU 1728 MHz) · tensorrt 10.16.2.10 conditions board: Jetson Orin Nano Engineering Reference Developer Kit Super (8 GB) · l4t: R39.2.1 · os: Ubuntu 24.04.4 LTS · python: 3.12.3 · desktop: running Inference cost only; not task success. 300 s run on synthetic 640x480 observations resized by the exported policy to 256x256; 4 denoising steps; latency is runtime-only over a pre-materialized observation ring. Whole-board RAM in use 5.78 GB (unified memory, desktop running). Actions were gated against the stock policy (cosine >= 0.99999) before timing. Three camera views, each with two frames (GR00T N1.7 temporal input).	T3 · community reported

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History

2026-10-25 fecccd1 Initial release: robot policy compatibility matrix · Danilo Znamerovszkij