Understanding Your System Capabilities

Before diving into local LLMs, you need to honestly assess what your hardware can handle. I’ve learned this the hard way - trying to run models that are too big for your system leads to crashes, slow performance, and frustration.

The Four Key Hardware Factors

System Memory (RAM) This is usually your biggest limiting factor. Models need to load entirely into memory, and if you don’t have enough RAM, your system will start swapping to disk, making everything painfully slow.

GPU Memory (VRAM) If you have a dedicated GPU, this can significantly speed up model inference. But GPU memory is usually more limited than system RAM, so you need to balance between speed and model size.

CPU Performance Even with GPU acceleration, your CPU still matters for coordinating tasks and handling parts of the model processing. Newer CPUs with more cores generally perform better.

Storage Speed Models can be large files (several gigabytes), so having fast storage (SSD) helps with loading times, especially when switching between different models.

Realistic Hardware Expectations

8GB RAM Systems

  • Can run smaller models (7B parameters or less)
  • Limited to basic quantized models
  • Expect slower performance
  • Good for testing and learning

16GB RAM Systems

  • Sweet spot for most users
  • Can handle 7B-13B parameter models comfortably
  • Some 34B models with heavy quantization
  • Good balance of capability and performance

32GB+ RAM Systems

  • Can run larger models (34B parameters)
  • Multiple model options
  • Better performance and quality
  • Room for other applications

GPU Considerations

  • 4GB VRAM: Helps with small models
  • 8GB VRAM: Significant improvement for medium models
  • 16GB+ VRAM: Can handle larger models with full GPU offloading

Testing Your System

The only way to know what works is to test. Start with smaller models and work your way up:

  1. Test a 7B model first - If this doesn’t run smoothly, stick to smaller models
  2. Monitor resource usage - Watch RAM and GPU usage during inference
  3. Check response times - If responses take more than 10-15 seconds, the model might be too big
  4. Test stability - Run longer conversations to ensure your system can handle sustained usage

Signs Your Model Is Too Big

  • System becomes unresponsive during loading
  • Responses take longer than 30 seconds
  • Your computer starts swapping heavily (disk activity increases)
  • Other applications become sluggish
  • The model produces garbled or incomplete responses

What “Quantization” Means for Requirements

Models come in different quantization levels that trade quality for size:

  • Q2: Smallest size, lowest quality
  • Q4: Good balance of size and quality
  • Q5/Q6: Higher quality, larger size
  • Q8: Nearly full quality, much larger

Choose quantization based on your memory constraints, not just because “higher is better.”