Distillers

🖹 HASH-SUM: b6065f3265675bc2f4a7874b4828c823 | 📅 Updated on: 2026-07-23 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: enough space for background apps and OS overhead Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unlocking the Power of GLM-4.7-Flash The GLM-4.7-Flash …

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🖹 HASH-SUM: 3c12792df8d3dfe577fc27592761f4de | 📅 Updated on: 2026-07-20 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 32 GB highly recommended for 26B+ GGUF models Disk: high-speed SSD 120 GB to cache model layers GPU: high memory bandwidth GPU for next-gen local AI pipeline Unveiling the Power of GLM-5-FP8 The cutting-edge language model, …

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📤 Release Hash: d8d2809ee3e63021bfc98bd868b25f79 • 📅 Date: 2026-07-18 Verify CPU: multi-threading optimized for fast prompt processing RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading The Qwen3-Omni-30B-A3B-Instruct: Unlocking the Power of Large Language …

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🖹 HASH-SUM: 621b070ed4e881f6cf1d8f2bbf5fdd27 | 📅 Updated on: 2026-07-20 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: CUDA Compute Capability 8.0+ required for flash-attention Paving the Way for Efficient Edge AIThe …

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🗂 Hash: d8d528038f65baf418ba3e45f7f0178c • Last Updated: 2026-07-14 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: required: 16 GB absolute minimum for small models Storage: extra room for future model updates and datasets Graphics: 12 GB VRAM minimum required for basic quantization The gemma-4-E4B-it-MLX-4bit model: A Breakthrough in Open-Source Language Models …

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🔍 Hash-sum: c7b72d925cb8d9052993accb4c64a89f | 🕓 Last update: 2026-07-17 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB highly recommended for 26B+ GGUF models Disk: 150+ GB for high-context vector database storage Graphics: 12 GB VRAM minimum required for basic quantization Gemma-4-31B-it-qat-w4a16-ct: Unveiling the Large Language Model’s Potential The Gemma-4-31B-it-qat-w4a16-ct is a revolutionary large …

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🔗 SHA sum: 6e2f911c3682c35f0b75b0b28d93b543 | Updated: 2026-07-15 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: at least 100 GB for multiple local LLM variants GPU: modern architecture (Ada Lovelace / Ampere minimum) Unlocking the Potential of Open-Source Language Models The gemma-4-26B-A4B-it-NVFP4 model represents …

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📦 Hash-sum → cb2500c635f78c6885278163eb68b685 | 📌 Updated on 2026-07-14 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB or higher for smooth 32k context lengths Disk: high-speed SSD 120 GB to cache model layers Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unlocking the Power of Deep Learning for OCR The …

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🔗 SHA sum: dfcf200cf15c1e036fb8516c2ada974e | Updated: 2026-07-13 Verify Processor: 6-core 3.5 GHz minimum required RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: free: 80 GB on system drive for scratch space GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Unlocking the Power of Molmo2-8B: A Revolutionary Vision-Language Model The …

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🧮 Hash-code: 7c4a2720dea8de95d08a2fee6cf5ec62 • 📆 2026-07-16 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB or higher for smooth 32k context lengths Disk: 150+ GB for high-context vector database storage GPU: high memory bandwidth GPU for next-gen local AI pipeline A Revolutionary Language Model for Code Generation The Qwen3-Coder-30B-A3B-Instruct model is a groundbreaking …

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