Local large language models often feel less capable than their cloud counterparts due to aggressive quantization that discards nuanced weights, reduced context windows limiting recall, and suboptimal prompt engineering. Users frequently overlook how system prompts and temperature settings drastically alter output coherence. The perceived drop in intelligence is rarely about model architecture alone, but rather inference constraints and interaction patterns.
- Aggressive quantization (e.g., Q4_K_M) trades subtle reasoning for speed, causing hallucinations.
- Context window limits force truncation, losing critical instructions from earlier in the conversation.
- Default system prompts are often generic; customizing them is essential for specialized tasks.
- Temperature settings above 0.7 increase creativity but reduce factual accuracy in local runs.
- Benchmark scores do not reflect real-world usability under constrained hardware limits.
