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Unsloth AI logo
Unsloth AI
✓ verifiedFreemium

Open-source library and desktop app for fast, memory-efficient local fine-tuning and inference of open LLMs.

1.1M visits/mo29K saves
PicoClaw logo
PicoClaw
✓ verifiedFree

Ultra-lightweight, self-hosted open-source AI assistant in Go that runs on sub-$10 hardware like Raspberry Pi with under 10MB RAM.

81K visits/mo
218K visits/mo
HEROZ logo
HEROZ
✓ verifiedPaid

A Japanese AI firm that grew from shogi-AI research into industry ML solutions and a generative-AI platform, HEROZ ASK.

1.9M visits/mo
Jan.ai logo
Jan.ai
✓ verifiedFree

Open-source desktop app for running AI chat models locally or via APIs, as a private ChatGPT alternative.

378K visits/mo609 saves
Pricing

No public pricing

No public pricing

No public pricing

No public pricing

No public pricing

Core features
  • Optimized LoRA/FFT/PT training kernels for 500+ models
  • Local offline model runner for Mac and Windows
  • No-code dataset creation from PDFs, CSVs, and JSON
  • Unlimited tool-calling and web search inside model runs
  • Data Recipes workflow to turn documents into training datasets
  • Export to safetensors or GGUF for llama.cpp, vLLM, Ollama
  • Multi-GPU support on paid tiers
  • Single self-contained binary requiring under 10MB RAM
  • Sub-1-second startup even on low-power processors
  • Support for 16+ chat channels including Telegram, Discord, Slack, WeCom
  • Compatibility with multiple LLM providers (OpenAI, Claude, DeepSeek, Gemini, etc.)
  • Runs on Raspberry Pi, RISC-V, ARM64, x86_64, Android, and Docker
  • Self-hosted design keeping data and configuration local
  • Gateway/API mode for connecting to chat platforms via MCP protocol
  • LLM API router
  • OpenAI API proxy
  • Model aggregation (OpenAI, Gemini, DeepSeek, Llama, Qwen, Claude, etc.)
  • Unified OpenAI API standard
  • Unlimited concurrency
  • Deep-learning and machine-learning core technology
  • HEROZ ASK generative-AI platform
  • BtoB and BtoC AI solutions
  • BLOOMWORKS product
  • Industry AI deployment case studies
  • Run open-source LLMs locally
  • Connect to online models (OpenAI, Claude, Gemini)
  • Private, offline-capable AI chat
  • Open source and self-hostable
  • Model library via Hugging Face
  • Cross-platform desktop app
Use cases
  • ML engineers fine-tuning open models on a single GPU for free
  • Teams building custom datasets from unstructured documents
  • Developers wanting to run and compare LLMs fully offline
  • Enterprises needing faster, more accurate multi-node training
  • Running a private AI assistant on minimal or embedded hardware
  • Local code assistance that keeps proprietary code off the cloud
  • Home automation and personal task scheduling via chat bots
  • Privacy-conscious users wanting self-hosted AI on low-cost devices
  • Integrating multiple AI models into applications using a single API
  • Accessing the latest AI models through a unified interface
  • Managing and scaling AI model usage with unlimited concurrency
  • Deploying generative AI in enterprises
  • Applying ML to industry-specific problems
  • AI-driven business transformation (DX)
  • Private local AI chat
  • Using multiple models in one app
  • Avoiding cloud data sharing
  • Experimenting with open models
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