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GigaBrain-0

Method overview: GigaBrain-0.7 pairs a VLA action system with a VLM planner and a world-model evaluator.

GigaBrain-0 from GigaAI: vla and vla action prediction; Verified; 2 variants.

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GigaBrain-0 is GigaAI's 3.5B VLA foundation policy with subtask prediction, released as GigaBrain-0-3.5B-Base and the 0.1 refresh under Apache-2.0. In this catalog the in-tree GigaBrain-0.1 runtime emitted a checkpoint-backed CUDA action_trace using converted LeRobot RoboTwin statistics, with PaliGemma tokenizer and the physical-intelligence FAST tokenizer staged.

The 0.1 variant is the promoted GPU route: a public pipeline wrote a checkpoint-backed CUDA action_trace on an 80 GB card using converted LeRobot RoboTwin statistics (about 20.8 GB allocated, ~55 s on the recorded probe). That is non-leaderboard runtime readiness. Official task-score parity still needs gated official task data and task-specific norm_stats / delta_mask. The 3.5B-base line passed a tensor GPU probe; promoted action-trace evidence currently covers 0.1.

Method overview: GigaBrain-0.7 pairs a VLA action system with a VLM planner and a world-model evaluator.
Method overview: GigaBrain-0.7 pairs a VLA action system with a VLM planner and a world-model evaluator.

The recorded paper is arXiv 2608.15875.

Project page: gigaai-research.github.io/GigaBrain-Challenge-2026. Code: open-gigaai/giga-brain-0.

Paper: arXiv 2608.15875. Project: gigaai-research.github.io/GigaBrain-Challenge-2026. Code: open-gigaai/giga-brain-0. Also required: gated google/paligemma-3b-pt-224 (Hugging Face auth) and physical-intelligence/fast. Runner: verified for that converted-stats scope. Aliases include giga-brain-0.1 and GigaBrain-0.1-3.5B-Base.

Build your run

Pipeline
GigaBrain0Pipelineworldfoundry.pipelines.component_pipelines
TaskInteractive Video interactive-video
Environmentworldfoundry-unified-cu128
DeviceCUDA 12.8
Runnerworldfoundry.evaluation.models.runners.pipeline:WorldFoundryPipelineRunner
Loadingfrom_pretrained
Invocationnative_or_standard_invocation
worldfoundry-eval run \
  giga-brain-0-3.5b-base \
  --pipeline.task-profile interactive-video \
  --json

Variants

  • giga-brain-0-3.5b-base
    StatusRunner parity pending

    vla.action_prediction

    Runtime profilegiga-brain-0

    Pipeline bindinggiga-brain-0

  • giga-brain-0.1-3.5b-base
    StatusRunner parity pending

    vla.policy_rollout

    Runtime profilegiga-brain-0

    Pipeline bindinggiga-brain-0

What it is for

Use giga-brain-0-3.5b-base for vision-language-action inference on this card. The recipe exists so you can launch the recorded policy profile.

A 3.5B policy on a PaliGemma tokenizer base using the FAST action tokenizer for discrete action encoding, plus subtask prediction heads. Architecture internals beyond the official repository pointer are not recorded in this repository.

Catalog tasks include vla, vla action prediction, vla policy rollout, and robot policy. Catalog aliases include giga_brain_0, gigabrain0, giga-brain-0.1, and GigaBrain-0-3.5B-Base among others. Also recorded: giga-brain-0.1-3.5b-base.

Recorded evaluation entry points include RoboTwin 2.0 (CUDA action trace was produced with converted RoboTwin statistics).

Runtime contract

Optional prompt, input_path, interactions, and fps; artifacts action_trace and giga_brain_0_action_trace.json.

CLI task profiles: interactive-video. WorldFoundry binds GigaBrain0Pipeline.

Start on giga-brain-0-3.5b-base. The family registers 2 catalog variants (cards above): giga-brain-0-3.5b-base (vla.action_prediction) — Runner parity pending; giga-brain-0.1-3.5b-base (vla.policy_rollout) — Runner parity pending. Family status: Verified.

The PaliGemma tokenizer model is gated (manual review) and requires Hugging Face auth. Official task-score parity remains separate because official task norm_stats/delta_mask assets are gated or not staged.

WorldFoundry in-tree GigaBrain-0.1 runtime emitted a checkpoint-backed CUDA action_trace using converted LeRobot RobotWin stats.

Running in WorldFoundry

Launch worldfoundry-eval run giga-brain-0-3.5b-base --pipeline.task-profile interactive-video to execute the policy. The command builder above switches --pipeline.task-profile among recorded tasks and still emits worldfoundry-eval run.

It runs in the unified environment worldfoundry-unified-cu128 (Python 3.11, CUDA 12.8).

Recorded weight repositories include open-gigaai/GigaBrain-0-3.5B-Base, open-gigaai/GigaBrain-0.1-3.5B-Base, and google/paligemma-3b-pt-224 among others. Prepare the environment and check local assets with bash scripts/inference/prepare_model_infer.sh giga-brain-0-3.5b-base. Add --download to fetch missing public checkpoints.

Recorded licenses include apache-2.0; status: Verified.