---
license: apache-2.0
tags:
  - robotics
  - vla
  - knowledge-distillation
  - model-compression
  - edge-deployment
  - action-chunking
  - multi-teacher
datasets:
  - lerobot/pusht
  - lerobot/libero
language:
  - en
library_name: forge
pipeline_tag: robotics
---

# FORGE-Nano: Compressed VLA for Real-Time Robot Control

<p align="center">
  <strong>7B VLA teacher &rarr; <1B student &rarr; 14.1 fps on edge GPU</strong>
</p>

## What is FORGE?

**FORGE** (Fast Optimized Robot Generation Engine) is a model distillation pipeline that takes any 7B+ Vision-Language-Action (VLA) model and compresses it to **<2GB for real-time edge deployment** on NVIDIA Jetson and Apple Silicon.

Part of the **ANIMA** agentic robotics AI stack by [Robot Flow Labs](https://robotflowlabs.com).

## Architecture

```
Teacher (7B VLA)
    |
    v
[SigLIP-SO400M] ---> [Bridge Attention] ---> [Qwen2.5-0.5B + LoRA] ---> [Action Head]
   (frozen)          (64 queries, 4L)       (rank=32/64)             (diffusion/flow)
   472.3M params     39.7M params           ~494M params             ~1.7M params
```

**Total: 967.9M params** (495.6M trainable, 472.3M frozen)

## Benchmark Results (4x NVIDIA L4 24GB)

### Student Variants
| Variant | Params | FP32 fps | FP16 fps | FP16 Speedup | Training Loss Reduction |
|---------|--------|----------|----------|--------------|------------------------|
| Nano (diffusion, LoRA=32) | 967.9M | 7.9 | **11.0** | 1.39x | 67.0% |
| Nano (diffusion, LoRA=64) | 972.3M | 7.9 | 10.8 | 1.37x | **76.9%** |
| Nano (flow, LoRA=32) | 967.9M | **8.2** | **12.6** | **1.54x** | 85.8% |
| Small (diffusion) | 2097.7M | 6.2 | 9.9 | -- | -- |
| Small (flow) | 2097.7M | 6.1 | **11.3** | -- | -- |

### Full Pipeline: Build -> Train -> Prune -> Deploy
| Configuration | Post-Prune Params | FP32 fps | FP16 fps | Loss Reduction |
|---------------|-------------------|----------|----------|----------------|
| Diffusion + p75 + INT4 | 830.8M | 10.0 | 12.0 | 41.4% |
| Flow + p50 + INT4 | **739.3M** | **14.1** | 7.8 | 76.3% |
| LoRA-64 + p90 + INT4 | 880.8M | 9.1 | 11.2 | **86.3%** |
| **Flow + LoRA-64 + p60** | **774.1M** | **12.7** | **14.1** | 75.7% |
| No prune + INT8 | 922.2M | 8.1 | 11.0 | 59.4% |

### Multi-GPU Scaling
| GPUs | FP32 b=16 | FP16 b=32 |
|------|-----------|-----------|
| 1 GPU | 9.3 fps | **33.6 fps** |
| 2 GPU | 13.5 fps | -- |
| 4 GPU | **13.6 fps** | 31.6 fps |

### Multi-Teacher Distillation
- **5 teachers** fit across 2 GPUs (22.7 GB total)
- Router learns optimal teacher weighting in <50 steps
- Best config: balanced (alpha_task=0.3) achieves **76.1% loss reduction**
- Supports: OpenVLA-7B, RDT2-FM, SmolVLA, BitVLA, Pi0

### Pruning Results
| Pruning Ratio | Layers | Params | FP32 fps | Speedup |
|---------------|--------|--------|----------|---------|
| No prune | 24 | 967.9M | 7.9 | 1.0x |
| 90% keep | 18 | 880.8M | 9.1 | 1.15x |
| 75% keep | 15 | 830.8M | 10.0 | 1.27x |
| 60% keep | 11 | 774.1M | 12.7 | **1.61x** |
| 50% keep | 9 | 739.3M | **14.1** | **1.78x** |

## Recommended Configurations

### Production (Edge Deployment)
```yaml
variant: nano
action_head: flow
lora_rank: 64
prune_ratio: 0.60
target_bits: 4
# Result: 774M params, FP16 14.1 fps, <600MB INT4
```

### Quality-First
```yaml
variant: nano
action_head: diffusion
lora_rank: 32
prune_ratio: 0.75
target_bits: 8
# Result: 830M params, 92.3% loss reduction
```

## Key Findings

1. **Flow matching head is 15% faster** than diffusion at FP16 inference (12.6 vs 11.0 fps)
2. **LoRA rank=64 trains 10% better** than rank=32 (76.9% vs 67.0% loss reduction) with negligible speed cost
3. **Aggressive pruning works**: 50% layer removal still produces a functional model at 14.1 fps
4. **FP16 autocast gives 1.4-1.5x speedup** for free — always use it in production
5. **Multi-teacher routing converges fast**: Router learns to weight teachers optimally in <50 steps

## Supported Teachers

| Teacher | Type | Params | Chunk Size |
|---------|------|--------|------------|
| OpenVLA-7B | Token-AR | 7.6B | H=1 |
| RDT2-FM | Diffusion | 1.2B | H=8 |
| SmolVLA | Parallel | 0.5B | H=1 |
| BitVLA | Quantized | 5.9B | H=1 |
| Pi0 | Flow | 3.0B | H=4 |

## Supported Robots

| Robot | DoF | Action Head | Horizon | Control Rate |
|-------|-----|-------------|---------|-------------|
| Franka Panda | 7 | Flow | H=8 | 20 Hz |
| ALOHA (bimanual) | 14 | Chunk | H=16 | 50 Hz |
| xArm | 6 | Flow | H=4 | 100 Hz |
| UR5e | 6 | Flow | H=4 | 125 Hz |

## Pipeline

```
Teacher Labels -> Knowledge Distillation -> Layer Pruning -> Quantization -> Edge Export
  (HDF5)           (LoRA + Bridge)        (Chunk-aware)    (INT4/INT8)     (TRT/ONNX/MLX)
```

## Usage

```bash
pip install anima-forge

# Full pipeline
forge pipeline --device cuda --variant nano --steps 5000

# Auto-detect model dimensions
forge autosense --model-dir /path/to/models

# Benchmark
forge benchmark run --device cuda

# Deploy
forge serve --port 8000
```

## Citation

```bibtex
@software{forge2026,
  title={FORGE: Fast Optimized Robot Generation Engine},
  author={Robot Flow Labs},
  year={2026},
  url={https://github.com/RobotFlow-Labs/anima-forge-distillation-pipeline}
}
```

## License

Apache 2.0