40. Small vs Large Models

Choose the right model size based on task complexity, cost, and hardware constraints.

By Jacques Botte, founder of Toptronic®. Last updated 12 September 2026.

The lesson

Small models (7B-13B parameters) run well on consumer hardware and are excellent for: classification, simple extraction, formatting, and fast local tasks.

Medium models (20B-40B) offer better reasoning while remaining runnable on high-end consumer GPUs with quantisation.

Large models (70B+) provide deep reasoning but require significant VRAM (40GB+) unless heavily quantised.

For Jacques: think of model size like memory selection - match the resource to the task. Use small fast models for simple work, large powerful models for complex reasoning.

Cloud frontier models (hundreds of billions of parameters) offer the best reasoning but at higher cost and latency.

Check yourself

Question 1: When is a small model (7B-13B) the better choice?
  1. Deep philosophical reasoning
  2. Safety-critical code review
  3. Fast classification, simple extraction, or local deployment — correct
  4. Creative fiction writing

Answer: Fast classification, simple extraction, or local deployment

Small models are excellent for fast, simple tasks and local deployment where speed matters.

Question 2: What is the main trade-off when using a 70B model versus a 7B model locally?
  1. Price only
  2. VRAM requirements and inference speed versus reasoning depth — correct
  3. Color output
  4. File format support

Answer: VRAM requirements and inference speed versus reasoning depth

70B models need more VRAM and are slower but provide deeper reasoning than 7B models.

Question 3: Why might you use a cheaper cloud model for initial drafting?
  1. They are always more accurate
  2. To save cost before reviewing with a premium model — correct
  3. They have larger context
  4. They run offline

Answer: To save cost before reviewing with a premium model

A cost-effective workflow uses cheaper models for drafts and premium models for final review.

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