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.
Both checkpoints are built on open sparse-MoE base models (Qwen3.5-MoE
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 <tier> 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).
- 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 — 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:
# 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 <dir> /
AutoEngine.from_pretrained(<dir>) 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) |
Qwen3.5-MoE 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).
*Peak active memory at short contexts (MLX allocator peak; expert weights
stream from SSD via mmap and are not resident). Long contexts add KV
cache: ~3.3 GiB on edge0-8b at 3.3k tokens.
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
License
Apache-2.0, including vendored third-party code (see NOTICE).
Edge0 是一个开源的流式 MoE（混合专家）推理框架，通过从 SSD 流式读取专家权重并提前一步预测路由，让你只用约 2.5-2.9 GB 的实际内存就能运行大型 MoE 模型，例如 35B 参数的模型。