Does GR00T N1.7 run on Bimanual SO-100/SO-101?
Scope: Training evidenced by public checkpoint(s); on-robot inference not documented by a primary source.
Capabilities
| Capability | State | Notes | Evidence |
|---|---|---|---|
| Train / fine-tune | works | ev-hub-orderdraconis-groot-v2, ev-hub-orderdraconis-groot-leo | |
| Real-robot inference | no verified evidence | — | |
| Simulation inference | no verified evidence | — | |
| Teleop + record | no verified evidence | — |
Evidence 2 sources; the cell shows the highest tier
- T3 · community reportedhub-checkpointsource 2026-10-11verified 2026-10-25 (today)
config.json type=groot with base_model_path nvidia/GR00T-N1.7-3B; train_config.json dataset OrderDraconis/phase2_align_combined_full_V2 (40000 steps), whose meta/info.json robot_type=bi_so_follower (249 episodes). The card only gives a templated lerobot-rollout command for bi_so_follower, no results.
https://huggingface.co/OrderDraconis/groot_phase2align_v2_c30 - T3 · community reportedhub-checkpointsource 2026-08-24verified 2026-10-25 (today)
config.json type=groot with base_model_path nvidia/GR00T-N1.7-3B; train_config.json dataset Janmeier820/align_fabric_dataset_combined (10000 steps), whose meta/info.json robot_type=bi_so_follower (173 episodes).
https://huggingface.co/OrderDraconis/groot_align_leo
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 | —conditionstorch_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 | —conditionstorch_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 | —conditionstorch_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 2.13.0conditionstorch_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 | —conditionstorch_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 | —conditionstorch_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 | —conditionstorch_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 | —conditionstorch_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 2.13.0conditionstorch_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 2.13.0conditionstorch_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 | —conditionstorch_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 2.13.0conditionstorch_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 | —conditionstorch_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 10.16.2.10conditionsboard: 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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