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Model: MikeKuykendall/gpt-oss-20b-moe-cpu-offload-gguf
Source: Original Platform

MOE-GGUF-README.md

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+# GPT-OSS 20B GGUF with MoE CPU Offloading Support
+
+This is a specialized conversion of OpenAI's GPT-OSS 20B model that has been tested and verified to work with MoE (Mixture of Experts) CPU offloading capabilities.
+
+## Key Features
+
+- **Full F16 precision** (13.8GB file size)
+- **MoE Architecture**: 32 experts, 4 active per token
+- **CPU Offloading Support**: Expert tensors can be offloaded to CPU for massive VRAM savings
+- **Verified Working**: Tested with shimmy feat/moe-cpu-offload branch
+- **Memory Efficiency**: Reduces GPU memory usage from ~15GB to ~2MB when using CPU offloading
+
+## Model Specifications
+
+- **Architecture**: gpt-oss
+- **Parameters**: 20.91B total, 3.6B active
+- **Context Length**: 131,072 tokens
+- **Quantization**: F16 base + MXFP4 expert weights
+- **Expert Count**: 32 experts, 4 used per token
+- **Sliding Window**: 128 tokens
+
+## Usage with MoE CPU Offloading
+
+This model requires a compatible inference engine that supports MoE CPU offloading. Tested with:
+- shimmy inference server (feat/moe-cpu-offload branch)
+- llama-cpp-rs with MoE CPU offloading support
+
+Example usage:
+```bash
+# Enable CPU offloading for all expert tensors
+./shimmy serve --cpu-moe --model gpt-oss-20b-f16.gguf
+```
+
+## Performance Characteristics
+
+- **VRAM Usage**: ~2 MiB (with CPU offloading) vs ~15GB (standard)
+- **Generation Speed**: Maintained quality with reasonable speed
+- **Memory Savings**: 99.9% VRAM reduction for large MoE models
+
+## Original Model
+
+Based on OpenAI's official GPT-OSS 20B model:
+- Source: https://huggingface.co/openai/gpt-oss-20b
+- License: Apache 2.0
+- Paper: https://arxiv.org/abs/2508.10925
+
+## Conversion Details
+
+Converted from the original HuggingFace format specifically to support MoE CPU offloading features in GGUF format while preserving the full model precision and MoE architecture.
+
+**Note**: This is an experimental build demonstrating MoE CPU offloading capabilities. Standard GGUF versions may not support this feature.