Does GR00T N1.7 run on ARX5?
We have found no published evidence for GR00T N1.7 on ARX5. Unknown means nobody has reported a result either way, so this page makes no claim.
Inferred: LeRobot supports ARX5 through the third-party plugin lerobot_robot_arx5 and GR00T N1.7 as --policy.type=groot since 0.6.0, so its tools connect them. No run has been published. Check action and state dimensions, camera count and compute before relying on it. Sources: ARX5 framework support, GR00T N1.7 framework support.
If you have run it (or failed to), a link to a public log, issue, checkpoint or video is enough. Open a pre-filled report. Failures are as valuable as successes.
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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