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title: GOBA-AI-Labs
emoji: 🧠
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GOBA-AI-Labs
Making large AI models accessible on consumer hardware.
We develop open-source tools for compressing Mixture-of-Experts (MoE) AI models. Our expert pruning technology reduces model sizes by 50-90% while preserving quality — enabling 400B+ parameter models to run on laptops with 24GB RAM.
PrunedHub Models
Calibration-based expert pruning with zero retraining. Drop-in replacements for llama.cpp.
| Model | Base | Size | Quality | Highlights |
|---|---|---|---|---|
| PrunedHub GPT-OSS-20B-28x | GPT-OSS-20B | 10.4 GB | MMLU 78% (lossless) | Zero quality loss, fits 16GB RAM |
| PrunedHub GPT-OSS-20B-27x-Zerobias | GPT-OSS-20B | ~9.4 GB | MMLU 77% (-1pp) | Experimental router optimization |
| PrunedHub Qwen3-30B-A3B-JP-80pct | Qwen3-30B-A3B | 14.0 GB | MMLU 79% (think-ON) | Language-aware pruning, Japanese quality preserved |
| PrunedHub Qwen3-Coder-Next-50pct | Qwen3-Coder-Next | 24.4 GB | MMLU 72% | 80B model in 24GB, outperforms Q2 quantization |
Our Approach
Traditional model compression relies on aggressive quantization, which degrades all computations uniformly. Our expert pruning takes a fundamentally different approach — removing entire redundant computation paths from MoE models while keeping the remaining experts at full precision.
- Calibration-based importance scoring — Expert importance measured through actual inference behavior, not static weight analysis
- Layer-adaptive expert allocation — Each layer retains a dynamically determined number of experts based on its contribution to quality
- Language-aware optimization — Automatic detection and protection of language-specialized experts
- Zerobias router optimization — Post-pruning router bias correction that extends the lossless compression frontier