Can a 30B Model Match a 700B? Debating the Limits of Model Compression
Logical_Two_7736 · reddit · 2026-08-02
Prompted by recent releases like DeepSeek V4 Flash, a developer sparked an in-depth discussion on whether there is a hard limit to how small models can get without losing intelligence.
Key Arguments:
- Capacity Floor: While better data, distillation, and MoE architectures make same-size models smarter, fundamental reasoning, broad generalization, and instruction following still require a baseline of parameter capacity.
- Cost Shifting: The cheap inference of small models might just shift costs to vastly more expensive training runs, synthetic data generation, or extended reasoning time.
- Benchmark Illusion: Small models matching larger ones on benchmarks might be overfitting to the tests. They often fall apart on rare knowledge, long tasks, or out-of-distribution prompts.
The author concludes that while architectural improvements will keep pushing the minimum required size down, the easy gains will eventually run out.
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