Struggling to figure out which local AI models your hardware can actually handle?
Here is a practical, no-fluff breakdown mapping local model parameter sizes to real-world hardware requirements and capabilities:
📱 1. Ultra-Lightweight (0.5B – 2B)
Hardware: Average mobile phones, entry-level tablets.
Best For: Snappy text completions, quick replies, and basic offline chat.
💻 2. Light Consumer (2B – 4B)
Hardware: High-end mobile phones, modern tablets, standard notebooks.
Best For: Fluid conversational assistants, basic summarization, and low-latency offline tasks.
⚙️ 3. Productivity & Professional (4B – 9B)
Hardware: Business-grade laptops, mid-range PCs, unified memory setups.
Best For: Daily writing assistance, moderate software development and debugging, and localized RAG over personal document libraries.
🚀 4. Advanced Workstation (9B+)
Hardware: Dedicated GPUs (4GB+ VRAM, ideally 16GB+), 16GB–32GB+ RAM, fast NVMe storage.
Best For: Model fine-tuning (LoRA/QLoRA), complex agentic workflows, multi-step tool use, and near-frontier offline coding.
Matching the right model size to your machine ensures optimal speed, token throughput, and efficiency without hitting memory bottlenecks.
What's your go-to local model size for your daily setup? Let’s discuss in the comments! 👇
#KAI - Learning for All
www.kohenoor.net
#ArtificialIntelligence #MachineLearning #kohenoorai #kenos #proedge #LocalAI #Hardware #TechTrends #OpenSourceAI
Here is a practical, no-fluff breakdown mapping local model parameter sizes to real-world hardware requirements and capabilities:
📱 1. Ultra-Lightweight (0.5B – 2B)
Hardware: Average mobile phones, entry-level tablets.
Best For: Snappy text completions, quick replies, and basic offline chat.
💻 2. Light Consumer (2B – 4B)
Hardware: High-end mobile phones, modern tablets, standard notebooks.
Best For: Fluid conversational assistants, basic summarization, and low-latency offline tasks.
⚙️ 3. Productivity & Professional (4B – 9B)
Hardware: Business-grade laptops, mid-range PCs, unified memory setups.
Best For: Daily writing assistance, moderate software development and debugging, and localized RAG over personal document libraries.
🚀 4. Advanced Workstation (9B+)
Hardware: Dedicated GPUs (4GB+ VRAM, ideally 16GB+), 16GB–32GB+ RAM, fast NVMe storage.
Best For: Model fine-tuning (LoRA/QLoRA), complex agentic workflows, multi-step tool use, and near-frontier offline coding.
Matching the right model size to your machine ensures optimal speed, token throughput, and efficiency without hitting memory bottlenecks.
What's your go-to local model size for your daily setup? Let’s discuss in the comments! 👇
#KAI - Learning for All
www.kohenoor.net
#ArtificialIntelligence #MachineLearning #kohenoorai #kenos #proedge #LocalAI #Hardware #TechTrends #OpenSourceAI