# Compute HW Guide for LeRobot Training

Rough sizing for training a LeRobot policy: how much VRAM each policy needs, what training time looks like, and where to run when local hardware isn't enough.

The numbers below are **indicative** — order-of-magnitude figures for picking hardware, not exact predictions. Throughput depends heavily on dataset I/O, image resolution, batch size, and number of GPUs.

## Memory by policy group

Policies cluster by backbone size; the groupings below give a single VRAM envelope per group instead of repeating numbers per policy. Memory scales roughly linearly with batch size; AdamW (the LeRobot default) carries optimizer state that adds ~30–100% over a forward+backward pass alone.

| Group      | Policies                                    | Peak VRAM (BS 8, AdamW) | Suitable starter GPUs             |
| ---------- | ------------------------------------------- | ----------------------: | --------------------------------- |
| Light BC   | `act`, `vqbet`, `tdmpc`                     |                  ~2–6GB | Laptop GPU (RTX 3060), L4, A10G   |
| Diffusion  | `diffusion`, `multi_task_dit`               |                 ~8–14GB | RTX 4070+ / L4 / A10G             |
| Small VLA  | `smolvla`                                   |                ~10–16GB | RTX 4080+ / L4 / A10G             |
| Large VLA  | `pi0`, `pi0_fast`, `pi05`, `xvla`, `wall_x` |                ~24–40GB | A100 40 GB+ (24 GB tight at BS 1) |
| Multimodal | `groot`, `eo1`                              |                ~24–40GB | A100 40 GB+                       |
| RL         | `sac`                                       |             config-dep. | See [HIL-SERL guide](./hilserl)   |

Memory-bound? Drop the batch size (~linear), use gradient accumulation to recover effective batch, or for SmolVLA leave `freeze_vision_encoder=True`.

## Training time

Robotics imitation learning typically converges in **5–10 epochs over the dataset**, not hundreds of thousands of raw steps. Once you know your epoch count, wall-clock is essentially:

```text
total_frames    = sum of frames over all episodes      # 50 ep × 30 fps × 30 s ≈ 45,000
steps_per_epoch = ceil(total_frames / (num_gpus × batch_size))
total_steps     = epochs × steps_per_epoch
wall_clock      ≈ total_steps × per_step_time
```

Per-step time depends on the policy and the GPU. The numbers in the table below are anchors — pick the row closest to your setup and scale linearly with `total_steps` if you train longer or shorter.

### Common scenarios

Indicative wall-clock for **5 epochs on a ~50-episode dataset (~45k frames at 30 fps × 30 s)**, default optimizer (AdamW), 640×480 images:

| Setup                                | Policy         | Batch | Wall-clock |
| ------------------------------------ | -------------- | ----- | ---------: |
| Single RTX 4090 / RTX 3090 (24 GB)   | `act`          | 8     |  ~30–60min |
| Single RTX 4090 / RTX 3090 (24 GB)   | `diffusion`    | 8     |      ~2–4h |
| Single L4 / A10G (24 GB)             | `act`          | 8     |      ~1–2h |
| Single L4 / A10G (24 GB)             | `smolvla`      | 4     |      ~3–6h |
| Single A100 40 GB                    | `smolvla`      | 16    |      ~1–2h |
| Single A100 40 GB                    | `pi0` / `pi05` | 4     |      ~4–8h |
| 4× H100 80 GB cluster (`accelerate`) | `diffusion`    | 32    |  ~30–60min |
| 4× H100 80 GB cluster (`accelerate`) | `smolvla`      | 32    |      ~1–2h |
| Apple Silicon M1/M2/M3 Max (MPS)     | `act`          | 4     |     ~6–14h |

These are order-of-magnitude figures. Real runs deviate by ±50% depending on image resolution, dataset I/O, dataloader threading, and exact GPU SKU. They are useful as "is this run going to take an hour or a day?" intuition, not as SLAs.

### Multi-GPU matters a lot

`accelerate launch --num_processes=N` is the easiest way to cut training time. Each optimizer step processes `N × batch_size` samples in roughly the same wall-clock as a single-GPU step, so 4 GPUs ≈ 4× speedup for compute-bound runs. See the [Multi GPU training](./multi_gpu_training) guide for the full setup.

Reference data points on a 4×H100 80 GB cluster (`accelerate launch --num_processes=4`), 5000 steps, batch 32, AdamW, dataset [`imstevenpmwork/super_poulain_draft`](https://huggingface.co/datasets/imstevenpmwork/super_poulain_draft) (~50 episodes, ~640×480 images):

| Policy      | Wall-clock | `update_s` | `dataloading_s` | GPU util | Notable flags                                                                                                                  |
| ----------- | ---------- | ---------: | --------------: | -------- | ------------------------------------------------------------------------------------------------------------------------------ |
| `diffusion` | 16m 17s    |      0.167 |           0.015 | ~90%     | defaults (training from scratch)                                                                                               |
| `smolvla`   | 27m 49s    |      0.312 |           0.011 | ~80%     | `--policy.path=lerobot/smolvla_base`, `freeze_vision_encoder=false`, `train_expert_only=false`                                 |
| `pi05`      | 3h 41m     |      2.548 |           0.014 | ~95%     | `--policy.pretrained_path=lerobot/pi05_base`, `gradient_checkpointing=true`, `dtype=bfloat16`, vision encoder + expert trained |

Training logs separate the full iteration into `dataloading_s` (`next(dl_iter)`), `preprocessing_s`
(image conversion and the policy pipeline), and `update_s` (the optimizer update). `step_s` covers all
three and drives `samples_per_s`. The benchmark above predates this split, so its `dataloading_s` includes
preprocessing.

### Schedule and checkpoints

If you shorten training (e.g. 5k–10k steps on a small dataset), also shorten the LR schedule with `--policy.scheduler_decay_steps≈--steps`. Otherwise the LR stays near its peak and never decays. Same for `--save_freq`.

## Where to run

VRAM is the first filter. Within a tier, pick by budget and availability — the `$`–`$$$$` columns are relative; check current pricing on the provider you actually use.

| Class                      | VRAM  | Tier   | Comfortable for                                             |
| -------------------------- | ----- | ------ | ----------------------------------------------------------- |
| RTX 3090 / 4090 (consumer) | 24 GB | `$`    | Light BC, Diffusion, SmolVLA. Tight for VLAs at batch 1.    |
| L4 / A10G (cloud)          | 24 GB | `$–$$` | Same envelope; common on Google Cloud, RunPod, AWS `g5/g6`. |
| A100 40 GB                 | 40 GB | `$$$`  | Any policy at reasonable batch sizes.                       |
| A100 80 GB / H100 80 GB    | 80 GB | `$$$$` | Multi-GPU clusters; large batches for VLAs.                 |
| **CPU only**               | —     | —      | Don't train. Use Colab or rent a GPU.                       |

### Hugging Face Jobs

[Hugging Face Jobs](https://huggingface.co/docs/hub/jobs) lets you run training on managed HF infrastructure, billed by the second, without owning a GPU. `lerobot-train` submits and streams the job for you — just add `--job.target=<flavor>` to a normal training command:

```bash
lerobot-train \
  --policy.type=act --dataset.repo_id=<USER>/<DATASET> \
  --policy.repo_id=<USER>/act_<task> \
  --job.target=a10g-large
```

Notes:

- Run `hf auth login` once before submitting, the job runs under your token.
- `--job.target` maps onto the table above: `t4-small`/`t4-medium` (T4, ACT only), `l4x1`/`l4x4` (L4 24 GB), `a10g-small/large/largex2/largex4` (A10G 24 GB scaled out), `a100-large` (A100). List the current catalogue with pricing via `hf jobs hardware`, or see [https://huggingface.co/docs/hub/jobs](https://huggingface.co/docs/hub/jobs).
- The job defaults to a `2d` (48h) timeout. Override it with `--job.timeout=4h` (or any other valid duration string) to shorten or extend the timeout. The job automatically stops when the command completes.
- For the full walkthrough — dataset upload, checkpoint streaming, resuming a run on a job — see the [imitation-learning training guide](./il_robots#train-using-hugging-face-jobs).
