Quantizers

📄 Hash Value: b1e308ef27b0a861bc62bb7fef3053e1 | 📆 Update: 2026-07-21 Verify Processor: high single-core performance needed for token latency RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: free: 80 GB on system drive for scratch space Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Unlocking the…

📊 File Hash: 3dcd6338dcb93500d42dcb158b20d7f7 — Last update: 2026-07-18 Verify Processor: 6-core 3.5 GHz minimum required RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: at least 100 GB for multiple local LLM variants Graphics: CUDA Compute Capability 8.0+ required for flash-attention The medgemma-27b-it model: A medical language model for…

📦 Hash-sum → 50ccfcbebc544fd3993c901ed0d2e581 | 📌 Updated on 2026-07-23 Verify Processor: high single-core performance needed for token latency RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: 12 GB VRAM minimum required for basic quantization The turbocharged z_image…

🖹 HASH-SUM: dec0a60268e2066cd9f78396a44b5559 | 📅 Updated on: 2026-07-16 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: required: 16 GB absolute minimum for small models Disk Space: at least 100 GB for multiple local LLM variants GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unlocking the Power…

📊 File Hash: 34ccc1986f759b4a41439a050eacd6b3 — Last update: 2026-07-15 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: required: fast PCIe 4.0 drive for instant boots Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Simplifying NLP with Qwen3.6-27B-MLX-5bit…