The most efficient one-page LoRA trainer for Anima 2B. Optimized for 6GB+ VRAM, featuring a smart dataset analyzer and real-time previews.
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Updated
May 31, 2026 - Python
The most efficient one-page LoRA trainer for Anima 2B. Optimized for 6GB+ VRAM, featuring a smart dataset analyzer and real-time previews.
One-click Windows installer for Z-Image Turbo AI image generation. Optimized for low-VRAM GPUs (4GB+). Features Gradio web UI, automatic setup, and GGUF model support.
Hierarchical RAG architecture scaling to 693K chunks on consumer hardware (4GB VRAM). Features 3-address routing, hybrid vector+graph fusion, and SetFit classification.
A ComfyUI Workflow for low vram users
"Adaptive Hybrid Quantization Framework for deploying 7B+ LLMs on low-VRAM devices (e.g., GTX 1050). Features surgical block alignment and Numba-accelerated inference.
Taiwanese Hokkien (Taigi) speech-to-text transcriber - MediaTek Breeze-ASR-26 with faster-whisper, tuned for RTX 3050 4GB low-VRAM GPUs. Gradio UI, CLI, Docker, SRT/VTT/TXT/JSON.
llama.cpp fork tuned for running modern models (Gemma-4, Qwen3.x) at full context on 12 GB Turing GPUs (RTX 2060/2070/2080, T4). TurboQuant KV cache (KTQ+VTQ, 2.78 bpw f16-quality), SWA-aware KV, MTP+n-gram speculation.
🎥 Generate high-quality videos on budget hardware with the Wan 2.2 14B Low-VRAM Workflow for ComfyUI, optimized for smooth performance and quick results.
Lightweight 6GB VRAM Gradio web app with auto-installer for running AuraFlow locally — no cloud, no clutter.
Simple FP16 image upscaler for all GPUs (low-mid end users)
Contains the notebooks and workflows configured to run inference from Wan 2.2 Animate with ComfyUI on Kaggle T4 GPUs smoothly
Perkunas AI Training Platform is a memory-aware model training and serving system for serious language model experimentation under tight hardware limits. It combines streaming training, rich telemetry, guarded recovery, checkpoint export, and OpenAI-compatible serving.
Unofficial AMD ROCm low-VRAM fork of Hunyuan3D-2.1 — 6-view PBR texture at ~10.5 GB peak on 20 GB AMD. See README_AMD_ROCM.md.
Audit local LLM function calling and agentic reliability. Visual tool-use benchmarking for quantized models on YOUR hardware.
A privacy-first Generative AI pipeline for prototyping 3D-style game assets on consumer hardware. Optimized for low-VRAM (4GB) GPUs using PyTorch, Diffusers, and Streamlit.
Lightweight Stable Diffusion engine with plugin-based pipelines, VRAM-safe execution, and full 4GB GPU support.
Wan2.2 (14B) Image-to-Video ComfyUI workflow optimized for 8GB VRAM. Features 2-stage dynamic GGUF switching to completely avoid OOM (<7.0GB peak).
A depth-guided, category-conditioned lightweight geometry completion network for indoor furniture reconstruction on consumer low-VRAM GPUs — 35M parameters, 88 MB, 0.5 s per object
ComfyUI nodes for Ideogram GGUF inference on 8GB VRAM.
Run Stable Diffusion locally on Intel iGPU/CPU with OpenVINO acceleration. Uncensored, Low VRAM, No NVIDIA required!
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