---
title: Edge0-8B-A1B-preview
canonical_url: "https://www.modelscope.ai/models/Edge0/Edge0-8B-A1B-preview"
md_url: "https://www.modelscope.ai/models/Edge0/Edge0-8B-A1B-preview.md"
repository: Edge0/Edge0-8B-A1B-preview
last_updated: 2026-09-18
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
architectures:
  - BailingMoeV3ForCausalLM
base_model:
  - inclusionAI/Ling-3.0-tiny-base
base_model_relation: adapter
parameters: 1.3B
tensor_type:
  - F16
  - BF16
  - U32
library_name:
  - lora
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 16
stars: 0
tags:
  - moe
  - edge-inference
  - prerouter
  - lora
  - ssd-offload
---

# Edge0-8B-A1B-preview

> Edge0-8B-A1B-preview - An open-source model by Edge0 on ModelScope. Edge0-8b-a1b Preview

Edge0/Edge0-8B-A1B-preview is a 1.3B-parameter text-generation model on ModelScope. licensed under apache-2.0. derived from inclusionAI/Ling-3.0-tiny-base.

- **Repository**: Edge0/Edge0-8B-A1B-preview
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 1.3B
- **Base model**: inclusionAI/Ling-3.0-tiny-base
- **Tags**: moe, edge-inference, prerouter, lora, ssd-offload
- **Downloads**: 16
- **Stars**: 0
- **Last updated**: 2026-09-18

Source: https://www.modelscope.ai/models/Edge0/Edge0-8B-A1B-preview

---

<div align="center">

<img src="20260908-223115.jpg" alt="edge0" width="100%">

<h1>Edge0-8b-a1b Preview</h1>

**An 8B-class sparse MoE that runs in phone-class memory.**

**1 GiB active memory · 25 tok/s · 4-bit**

[![GitHub](https://img.shields.io/badge/GitHub-Edge0--AI%2Fedge0-black?style=for-the-badge&logo=github)](https://github.com/Edge0-AI/edge0)
[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--35b--a3b--preview-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-35b-a3b-preview)
[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Edge0--8b--a1b--preview-yellow?style=for-the-badge)](https://huggingface.co/Edge0/Edge0-8b-a1b-preview)
[![ModelScope](https://img.shields.io/badge/ModelScope-Edge0--35B--A3B--preview-624AFF?style=for-the-badge&logo=modelscope&logoColor=white)](https://www.modelscope.cn/models/Edge0/Edge0-35B-A3B-preview)
[![ModelScope](https://img.shields.io/badge/ModelScope-Edge0--8B--A1B--preview-624AFF?style=for-the-badge&logo=modelscope&logoColor=white)](https://www.modelscope.cn/models/Edge0/Edge0-8B-A1B-preview)
[![arXiv](https://img.shields.io/badge/arXiv-2609.18063-B31B1B?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2609.18063)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue?style=for-the-badge)](https://github.com/Edge0-AI/edge0/blob/main/LICENSE)

</div>


**Edge0-8b-a1b** — an 8B MoE LLM that runs at viable speed in under **1 GiB of active memory**, 
via the [edge0](https://github.com/Edge0-AI/edge0) streaming inference framework.


> **Preview status:** this is an early preview release of the edge0
> pipeline. The checkpoint ships as int4 quantization plus LoRA and
> prerouter adapters trained for this framework.

<div align="center">

<video
  src="https://huggingface.co/Edge0/Edge0-35B-A3B-preview/resolve/main/20260910-105854.mp4"
  controls
  playsinline
  preload="metadata"
  width="80%">
</video>

</div>

## Highlights

- **Runs in phone-class memory**: the full 4-bit checkpoint stays on
  storage and experts are streamed on demand, so only the active
  weights are in RAM — under **1 GiB**, with no sharding and no
  upfront download of the weights into memory.
- **Fast enough for interactive use**: 25 tok/s decode;
  long prompts fill in at 1400 tok/s.
- **Quality kept after quantization**: Recover-LoRA distillation keeps
  the int4 model within **2.8 points** of its fp16 base (and above it
  on MMLU-Pro).
- **Works out of the box**: base, LoRA and prerouter adapters ship
  together and load automatically via `edge0`.


Three mechanisms make this work:

- **SSD expert offload**: expert weights are streamed from storage on
  demand — fetched only as routed, so RAM holds just the active
  weights.  Peak memory is bounded by the active set, not the
  parameter count.
- **Prerouter**: a trained head predicts expert routing one step
  ahead, so expert loads overlap the forward pass instead of stalling
  it — **up to +59%** decode throughput; the gain grows with storage
  latency, model size, and routed width *K*.
- **Recover-LoRA**: the int4 base is frozen and LoRA adapters are
  trained by distillation from the FP teacher, recovering most of the
  quantization loss at 4-bit (see Quality below).  Adapters stay
  unmerged: one read-only base serves multiple adapter sets.

## Model summary

| | |
|---|---|
| Base model | inclusionAI Ling 3.0 tiny (bailing hybrid, MLA + MoE, ≈7.9B total / ≈1.2B active) |
| Quantization | 4-bit |
| Layers | 24 |
| Experts / active per token | 128 / 8 (K=8) |
| Hidden size | 1536 |
| Context | 128k |
| Thinking mode | yes (chat template) |
| License | Apache 2.0 |
| Framework | [edge0](https://github.com/Edge0-AI/edge0) (MLX backend) |
| Contents | base checkpoint + `lora_edge0_8b.safetensors` + `prerouter_edge0_8b.safetensors` |

The LoRA and prerouter adapters are co-located with the base checkpoint
and load automatically — this repository is a complete, ready-to-run
model directory for `edge0`.

## Quality

All benchmarks were run by us with [OpenCompass](https://github.com/open-compass/opencompass)
under identical settings and parameters for both models. The loss of the
edge0 pipeline (int4 + adapters) relative to the fp16 base model is
small: **2.8 points on average**, with MMLU-Pro above the base. Max 100:

| Benchmark | edge0-8b (int4) | Ling 3.0 tiny (fp16) |
|---|---:|---:|
| AIME 2026 | 63.3 | 73.3 |
| HumanEval | 91.5 | 92.7 |
| GPQA-Diamond | 70.7 | 71.2 |
| MMLU-Pro | 70.1 | 65.8 |
| IFBench | 53.9 | 60.6 |
| **Average** | **69.9** | **72.7** |

## Performance

Measured with `examples/bench.py` on a Mac mini M4 Pro, 24 GB:

| Decode speed | Prefill throughput (cold / warm) | Peak active memory |
|---|---|---|
| 23.9–25.3 tok/s | 500 / 1428 tok/s | 1.0 GiB |

## Use cases

- Edge / on-device inference where GPU VRAM is scarce and storage is
  fast (NVMe, internal flash).
- Batch serving on a single commodity machine — one read-only base
  serves many LoRA adapter sets without re-quantization.
- Multilingual chat and reasoning with thinking mode enabled by the
  bundled chat template.

## Limitations

- Preview release: coverage and quality are still being extended; the
  model is primarily tuned for the languages of the base model.
- Agent capability: this preview release is not yet optimized for
  agentic tasks — tool use, multi-step planning, and long-horizon
  autonomy are currently weak. The full release will substantially
  strengthen agent capability.
- The MLX backend currently targets Apple Silicon; other backends are
  on the edge0 roadmap.

## Quick start

```bash
pip install -e 'git+https://github.com/Edge0-AI/edge0.git#egg=edge0[fetch]'

# Download this repository into a local directory
huggingface-cli download Edge0/Edge0-8b-a1b-preview --local-dir ./Edge0-8b-a1b-preview

# Run it
export EDGE0_8B_MODEL=$PWD/Edge0-8b-a1b-preview
edge0 chat --name edge0-8b --prompt "Introduce yourself"

# Or serve an OpenAI-compatible HTTP API
edge0 serve --name edge0-8b --port 8083
```

For full usage (Python API, streaming options, prerouter details), see the
[edge0 documentation](https://github.com/Edge0-AI/edge0#documentation).

## License

Apache 2.0. See [LICENSE](https://github.com/Edge0-AI/edge0/blob/main/LICENSE).

## Citation

If you find Edge0 useful in your research, please cite our paper:

```bibtex
@misc{lin2026halfmemorywallserving,
      title={The Other Half of the Memory Wall: Serving 35B MoEs from SSD with Trained Routing Prediction},
      author={Yu Lin and Yiming Wang and Runyuan Cai and Hanze Liu and Xiaodong Zeng},
      year={2026},
      eprint={2609.18063},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2609.18063},
}
```