🧠 Introducing GGML-Win64-Mem-Framework

We are excited to release ggml-win64-mem-framework, a production-ready C++ framework for GGML memory optimization, security, and monitoring on Windows. This project was created to bridge the gap between research prototypes and enterprise-grade deployments, with a focus on high-performance inference and sustainable operations.

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🚀 Key Features

• Memory Optimization

  • Arena Allocator (RAII + Large Page fallback)
  • VRAM Pool with async free & CUDA Graph acceleration
  • NUMA-aware allocation
  • HMM (cudaMallocManaged + Prefetch)

• Security Enhancements

  • Zero-Copy IPC with AES-256-GCM encryption
  • TPM 2.0 PCR Extend logging
  • DLL SafeLoad + Code Signing automation

• Operations & Monitoring

  • Hot Reload (zero-downtime model swap)

  • Rollback Agent (automatic failover & recovery)

  • Grafana plugin for real-time monitoring:

    • Latency, throughput, VRAM usage, GPU temperature, carbon footprint

• ESG Reporting

  • JSON export for latency, energy usage, carbon emissions
  • Renewable energy percentage tracking
  • Compatible with GRI/SASB frameworks for sustainability disclosure

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📊 Benchmark Snapshot

With CUDA Graphs + VRAM Pooling, we achieved up to 2.1x throughput improvements on batch inference.

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🔧 Installation (One-Click Setup)

# Run as Administrator
git clone https://github.com/sadpig70/ggml-win64-mem-framework.git
cd ggml-win64-mem-framework
.\install-all.ps1

The script will:

• Install Chocolatey + vcpkg
• Configure CUDA, CMake, Hyperscan, Dr.Memory
• Enable Large Pages & Lock Pages privilege
• Harden DLL search policies
• Set up Code Signing certificates
• Build & verify the framework

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🌍 Why It Matters

Running large language models on Windows is often challenging due to fragmentation in memory management and lack of integrated security features. This framework provides a turn-key solution for:

• Developers – plug-and-play building blocks (Arena, VRAM Pool, Zero-Copy IPC)
• Researchers – reproducible benchmarking environment with ESG reporting
• Enterprises – secure, sustainable, production-ready infrastructure

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📥 Get Started

• GitHub: ggml-win64-mem-framework

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🙌 Acknowledgments

Developed by Jung Wook Yang (정욱님) with the SevCore team, combining advanced AI system orchestration, low-level C++ engineering, and sustainability principles.

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✍️ This project aims to empower the Hugging Face community to run efficient, secure, and eco-friendly LLM inference on Windows.

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