🗂️ AI & Agents · View mindmap

• “llm”
  • “local-inference”
  • “quantization”
  • “instruction-following”
  • “video-generation”
  • “pinokio” 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 instruction-following tasks when model-efficiency reduces its footprint for 48GB NVIDIA GPU deployment.
• Gemma 2 27B (quantized): Efficient balance of size and performance for instruction tasks on consumer-grade hardware.
• Qwen 2 72B (quantized): High-performing alternative for complex instruction following with quantized optimization.
• Mistral Large (quantized): Suitable for instruction tasks when quantized for 48GB VRAM constraints.
• gpt-oss 20B (quantized): OpenAI’s open-weight model demonstrates strong instruction-following capabilities when quantized for 48GB VRAM deployment.

Local Video Generation

• Pinokio: A specialized model for local video generation tasks.

Additional Notes

• For running well-instructed small Large Language Models (LLMs) on a 48GB VRAM NVIDIA GPU, Llama 3.1 70B (quantized) is a strong contender.
• Other viable options include quantized versions of Gemma 2 27B, Qwen 2 72B, and Mistral Large.
• These models, when properly quantized to reduce their size, can effectively run on a 48GB VRAM hardware.

Source Notes

• 2026-04-14: “But OpenClaw is expensive…”
• 2026-04-08: What Is Llama.cpp? The LLM Inference Engine for Local AI
• 2026-04-07: Benchmarking SLMs Identifying 4GB General Problem Solving Champions · ▶ source
• 2026-04-10: 1 Bit LLMs BitNet Bonsai and Efficient On Device Deployment · ▶ source
• 2026-04-12: Kimi K25 Local AI Cluster Performance vs ChatGPT and Claude · ▶ source
• 2026-04-21: Local Mistral · ▶ source
• 2026-05-01: Local vs. Cloud LLMs for Code Generation: Performance Comparison for an Interpreter Task