White Hat Group

Private Local LLMs

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Here are the 10 highest-rated open-source LLMs capable of running locally while ensuring private, offline operation—meaning they do not report data, train externally, or inform third parties. These models prioritize user privacy and can be deployed on consumer-grade hardware (e.g., GPUs with ≥10GB VRAM or CPUs with quantization). Rankings are based on benchmark performance (MMLU, MT-Bench), versatility, and community adoption:

Top 10 Private, Local LLMs

  1. Meta Llama 3.1 8B
    • Parameters: 8B
    • Highlights: Balanced performance for reasoning and coding, optimized for CPU/GPU via GGUF quantization. Surpasses GPT-3.5 in efficiency.
    • Privacy: Fully offline with Hugging Face transformers or llama.cpp.
  2. Google Gemma 2 7B
    • Parameters: 7B
    • Highlights: Outperforms GPT-3.5 Turbo in coding tasks. Runs locally via gemma.cpp with 4-bit quantization.
  3. OpenHermes 2.5 Mistral 7B
    • Parameters: 7B
    • Highlights: Fine-tuned for conversational AI and role-play. Achieves 64.5+ MT-Bench scores on consumer GPUs.
  4. Zephyr 7B
    • Parameters: 7B
    • Highlights: Instruction-tuned variant of Mistral-7B. Excels in chat and reasoning on low-resource hardware.
  5. Qwen 1.5 7B
    • Parameters: 7B
    • Highlights: Multilingual support, strong in math and code. Runs offline with Alibaba’s qwen.cpp.
  6. StableLM 2 12B
    • Parameters: 12B
    • Highlights: Multilingual (7 languages), ideal for structured data tasks. Apache 2.0 licensed.
  7. MythoMax L2 13B
    • Parameters: 13B
    • Highlights: Storytelling and creative tasks. Merges Llama 2 with MythoLogic, optimized for role-play.
  8. OpenOrca Platypus2 13B
    • Parameters: 13B
    • Highlights: Scores 64.5+ on MMLU/ARC benchmarks. Runs on RTX 3060 GPUs.
  9. Vicuna v1.5 13B
    • Parameters: 13B
    • Highlights: Fine-tuned for 16K context. Ideal for long-form Q&A on modest hardware.
  10. Vicuna 33B
    • Parameters: 33B
    • Highlights: Higher reasoning capability. Requires 16GB+ VRAM but runs privately via llama.cpp.

Key Considerations

  • Privacy: All models use permissive licenses (MIT/Apache 2.0) and can operate offline without telemetry.
  • Hardware: Smaller models (7B–13B) run on mid-tier GPUs; larger models (33B) need high-end hardware. Use GGUF quantization for CPU-only setups.
  • Deployment: Tools like llama.cpp, text-generation-webui, or Ollama enable local execution.

For coding-specific tasks, Gemma 2 7B and Llama 3.1 8B are top choices. For multilingual support, Qwen 1.5 7B and StableLM 2 12B excel.