Edge0-AI/Edge0

edge0 is an open-source streaming MoE inference framework. It generalizes the production-proven recipe — SSD expert offload + Recover-LoRA + prerouter routing prediction — into an extensible framework. The backend is isolated by design: the current MLX backend runs on Apple Silicon, and additional platforms (CUDA, …) plug into the same core abstractions.

Two model tiers ship with the framework. Each tier is an end-to-end release: the released checkpoint, the trained LoRA adapters, and the trained prerouter heads work together as one unit.

| Tier | Released checkpoint | Inference profile | |---|---|---| | edge0-35b | Edge0/Edge0-35B-A3B-preview · ModelScope | 4-bit, 40 layers, 256 experts, prerouter K=4 | | edge0-8b | Edge0/Edge0-8B-A1B-preview · ModelScope | 4-bit, 24 layers, 128 experts, prerouter K=8 |

Both checkpoints are built on open sparse-MoE base models (Qwen3.6-35B-A3B and the Ling 3.0 bailing hybrid respectively) and ship with the LoRA and prerouter training done for this framework — the adapter files are co-located with each checkpoint and load automatically, so edge0 serve runs the trained pipeline out of the box.

Requirements

• OS / hardware: the MLX backend runs on macOS with Apple Silicon

(M1/M2/M3/M4). The CUDA backend is on the roadmap — no other platforms are supported yet.

• Python: 3.10+ (3.12 recommended).
• MLX: mlx==0.30.6 / mlx-metal==0.30.6 with mlx-lm==0.31.0 (see

pyproject.toml). Garbled, mixed-language output on Apple A18 / A18 Pro means an older mlx: pip install 'mlx==0.30.6' 'mlx-metal==0.30.6' (#8).

• Memory: ~2.9 GB peak active memory for edge0-35b, ~1.0 GB for

edge0-8b (short contexts; see Benchmark). Add headroom for the OS, tokenizer, and long-context KV growth.

• Disk: the 4-bit checkpoints are ~23 GB (edge0-35b) and ~4.2 GB

(edge0-8b); expert weights are mmapped and read on demand, they are not loaded into RAM up front.

Design

• transformers-style usage: AutoModel / AutoConfig / AutoEngine

resolve the tier from the model name;

• Backend isolation: all MLX code lives under edge0/backends/mlx/;

the core logic (model specs, prerouter, streaming expert pool, server) depends only on the backend facade (edge0/backends/base.py), so a new backend implements the same facade (backends/cuda/ is a reserved slot) with zero changes to core code;

• Adapters as safetensors: LoRA and prerouter weights are

.safetensors files with provenance metadata (source, version, owner layers), resolved from the model directory or artifacts/;

• Model + adapters in one directory: a model directory holds both

the base checkpoint (config.json / model*.safetensors / tokenizer) and that model's adapters; upgrading adapters swaps adapter files only — the base stays read-only and is never merged.

Core mechanisms

• SSD expert offload: expert weights are streamed from storage on

demand; 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). Adapters stay unmerged: one read-only base serves multiple adapter sets.

Quick start

1) Install

# Python >= 3.10; the MLX backend requires macOS with Apple Silicon
python3.12 -m venv .venv && .venv/bin/pip install -e '.[dev,fetch]'

2) Download a model

The two tiers are published on Hugging Face and ModelScope — each repo bundles the base checkpoint and the trained LoRA + prerouter adapters in one directory, so a single download is a ready-to-run model:

• Edge0/Edge0-35B-A3B-preview (~23 GB) · ModelScope mirror
• Edge0/Edge0-8B-A1B-preview (~4.2 GB) · ModelScope mirror

# with the repo's helper (defaults to the two repos above):
.venv/bin/python scripts/fetch_models.py --tier edge0-35b --target-dir models
.venv/bin/python scripts/fetch_models.py --tier edge0-8b --target-dir models

or directly with the CLI:
.venv/bin/huggingface-cli download Edge0/Edge0-35B-A3B-preview     --local-dir models/edge0-35b
.venv/bin/huggingface-cli download Edge0/Edge0-8B-A1B-preview     --local-dir models/edge0-8b

Either way you end up with a directory like:

models/edge0-35b/
├── config.json, model-*.safetensors, tokenizer files   # base checkpoint
├── lora_edge0_35b.safetensors          # trained LoRA adapters
└── prerouter_edge0_35b.safetensors     # trained prerouter heads

3) Point edge0 at it

Tier names resolve to local directories via environment variables (where you put the download is up to you):

export EDGE0_35B_MODEL=$PWD/models/edge0-35b
export EDGE0_8B_MODEL=$PWD/models/edge0-8b

Or skip the env vars entirely and pass the directory directly — the tier is auto-detected from the checkpoint's config.json:

edge0 demo models/edge0-35b
edge0 serve models/edge0-8b

4) Run

# quick demo
edge0 demo edge0-35b
serve (OpenAI-compatible /v1/chat/completions)
edge0 serve edge0-35b

curl http://127.0.0.1:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"messages":[{"role":"user","content":"Hello!"}],"max_tokens":32}'
5) One-shot chat (pass --max-new to cap length; add --show-thinking to
print the model's reasoning block too)
edge0 chat edge0-35b --prompt "Explain streaming inference in one sentence."

python -m edge0 ... is equivalent to edge0 ....

Python API

from edge0 import AutoEngine
from edge0.server.chat import ChatMessage, ChatRequest, ChatSession
engine = AutoEngine.from_pretrained("/path/to/model")  # tier auto-detected
req = ChatRequest(
    model=engine.name,
    messages=[ChatMessage(role="user", content="Hello!")],
    max_tokens=64,
)
tokens, meta = ChatSession(engine, req).run()
print(engine._tok.decode(tokens))
engine.close()   # release mmaps / expert cache

examples/demo.py is the same minimal walkthrough (edge0 demo runs this exact path).

Models and adapters

• Checkpoint: the original model directory (config.json,

model*.safetensors, tokenizer). edge0 serve / AutoEngine.from_pretrained() detect the tier from config.json.

• Adapters (LoRA + prerouter, safetensors) are resolved from either

location automatically:

• the model directory (recommended): side by side with the base, e.g.

lora_edge0_35b.safetensors + prerouter_edge0_35b.safetensors;

• artifacts/ (repo root, gitignored): convert once from

training-side npz exports via edge0 convert-adapters --npz-dir ....

• The published model repos bundle both the base checkpoint and the

current default adapter release, so scripts/fetch_models.py produces a ready-to-run model directory. Check each model's doc page for its adapter provenance (training data, owner-layer layout).

• Both adapters are required for the prerouter + LoRA pipeline; if a

file is missing, edge0 fails with a clear message (or pass --no-prerouter / --no-lora to run the plain base model).

Quality

All benchmarks were run by us with OpenCompass under identical settings and parameters for both the edge0 models (int4 + trained adapters + prerouter routing) and the original fp16 base models. The loss of the edge0 pipeline is small: 3.9 points on average for edge0-35b, 2.8 for edge0-8b (MMLU-Pro is even above the base). Max 100:

| Benchmark | edge0-35b (int4) | Qwen3.6-35B-A3B (fp16) | edge0-8b (int4) | Ling 3.0 tiny (fp16) | |---|---:|---:|---:|---:| | AIME 2026 | 86.6 | 92.7 | 63.3 | 73.3 | | HumanEval | 90.9 | 95.1 | 91.5 | 92.7 | | GPQA-Diamond | 79.8 | 81.8 | 70.7 | 71.2 | | MMLU-Pro | 81.0 | 84.6 | 70.1 | 65.8 | | IFBench | 57.9 | 61.7 | 53.9 | 60.6 | | Average | 79.2 | 83.2 | 69.9 | 72.7 |

Benchmark

Measured with examples/bench.py (3.3k-token prompt prefill → 10 sampled warmup steps → 200 timed sampled decode tokens, 2 runs per tier):

| Tier | Decode speed | Prefill throughput (cold / warm)* | Peak active memory | Test machine | |---|---|---|---|---| | edge0-35b | 14.9–17.7 tok/s | 113 / 140 tok/s | 2.9 GiB | Mac mini M4 Pro, 24 GB | | edge0-8b | 23.9–25.3 tok/s | 500 / 1428 tok/s | 1.0 GiB | Mac mini M4 Pro, 24 GB |

*Cold = first request after process start (expert weights fault in from SSD); warm = subsequent requests (page cache resident). Prefill numbers are throughput over a ~3.3k-token prompt (BENCH_LONG=1).*

Reproduce:

python examples/bench.py edge0-35b    # via $EDGE0_35B_MODEL
python examples/bench.py edge0-8b    # via $EDGE0_8B_MODEL

Tests

pytest                 # unit tests (no real weights)
EDGE0_8B_MODEL=/path/to/edge0-8b pytest -m slow -q
                        # real-weight generation; missing tiers are skipped
.venv/bin/python scripts/e2e_smoke.py \
  --qwen-dir /path/to/edge0-35b --ling-dir /path/to/edge0-8b
                        # staged vs exact consistency + generation smoke
scripts/generate_example.py   # full-pipeline API example
examples/demo.py       # minimal API walkthrough

Documentation

• Architecture
• Attention / MoE / SSD streaming / prerouter
• Adding a model
• edge0-35b / edge0-8b

License

Apache-2.0, including vendored third-party code (see NOTICE).