🗂️ AI & Agents · View mindmap

• “llm”
  • “local-inference”
  • “quantization”
  • “instruction-following”
  • “video-generation”
  • “pinokio”
  • “Alibaba OvisOCR2”
  • “Qwen FableVibes”
  • “Poolside Laguna S 2.1” group: model-efficiency-compression

Local Inference

Running large language models (LLMs) directly on user-owned hardware without cloud dependency, enabling privacy, offline use, and reduced latency.

Recommended Models for Instruction Following (48GB VRAM)

• Llama 3.1 70B (quantized): Meta’s model excels in general-purpose tasks when quantized for local deployment.
• Qwen based models: See FableVibes for benchmarking data on Qwen-based performance in local environments.
• OvisOCR2: Specialized for document parsing and OCR tasks, leveraging Alibaba’s infrastructure for efficient local execution.

Agentic Coding on Local Hardware

Recent advancements in agentic coding models allow for complex, multi-step reasoning tasks to be executed locally.

• Poolside’s Laguna S 2.1: Efficient Open-Source Agentic Coding for Local Hardware:
  • Developed by Poolside, this model utilizes an 118 billion parameter Mixture-of-Experts (MoE) architecture.
  • Designed specifically for efficient open-source agentic coding on local hardware, balancing high capability with resource constraints.
  • Focuses on reducing the latency and hardware requirements typically associated with large-scale agentic workflows.

References

• Poolside’s Laguna S 2.1: Efficient Open-Source Agentic Coding for Local Hardware