Edge0/Edge0-8B-A1B-preview

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Edge0-8b-a1b Preview

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

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

Edge0-8b-a1b — an 8B MoE LLM that runs at viable speed in under 1 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.

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 (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 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 directoryhuggingface-cli download Edge0/Edge0-8b-a1b-preview --local-dir ./Edge0-8b-a1b-preview
# Run itexport EDGE0_8B_MODEL=$PWD/Edge0-8b-a1b-previewedge0 chat --name edge0-8b --prompt "Introduce yourself"
# Or serve an OpenAI-compatible HTTP APIedge0 serve --name edge0-8b --port 8083

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

License

Apache 2.0. See LICENSE.

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

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

Use cases

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

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

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 directoryhuggingface-cli download Edge0/Edge0-8b-a1b-preview --local-dir ./Edge0-8b-a1b-preview
# Run itexport EDGE0_8B_MODEL=$PWD/Edge0-8b-a1b-previewedge0 chat --name edge0-8b --prompt "Introduce yourself"
# Or serve an OpenAI-compatible HTTP APIedge0 serve --name edge0-8b --port 8083

pip install -e 'git+https://github.com/Edge0-AI/edge0.git#egg=edge0[fetch]'
# Download this repository into a local directoryhuggingface-cli download Edge0/Edge0-8b-a1b-preview --local-dir ./Edge0-8b-a1b-preview
# Run itexport EDGE0_8B_MODEL=$PWD/Edge0-8b-a1b-previewedge0 chat --name edge0-8b --prompt "Introduce yourself"
# Or serve an OpenAI-compatible HTTP APIedge0 serve --name edge0-8b --port 8083

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