Edge0-35B-A3B-preview

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A 35B-class sparse MoE that runs in phone-class memory.

3 GiB active memory · 15 tok/s · 4-bit

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Edge0-35b-a3b — a 35B MoE LLM that runs at viable speed in under 3 GiB of active memory, via the 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.

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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 3 GiB, with no sharding and no upfront download of the weights into memory.
• Fast enough for interactive use: 15 tok/s decode; long prompts fill in at 140 tok/s.
• Quality kept after quantization: Recover-LoRA distillation keeps the int4 model within 3.9 points of its fp16 base.
• 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

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 under identical settings and parameters for both models. The loss of the edge0 pipeline (int4 + adapters) relative to the fp16 base model is small: 3.9 points on average. Max 100:

Performance

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

*Short contexts; long contexts add KV cache. Expert weights stream from SSD on demand and are not resident.

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.
• Long contexts grow the KV cache; use shorter contexts to keep peak memory at 3 GiB.

Quick start

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/Edge0-35b-a3b-preview --local-dir ./Edge0-35b-a3b-preview

# Run it
export EDGE0_35B_MODEL=$PWD/Edge0-35b-a3b-preview
edge0 chat --name edge0-35b --prompt "Introduce yourself"

# Or serve an OpenAI-compatible HTTP API
edge0 serve --name edge0-35b --port 8085

For full usage (Python API, streaming options, prerouter details), see the edge0 documentation.

License

Apache 2.0. See LICENSE.