𝗠𝗼𝗱𝗲𝗹 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: 𝗦𝘁𝗼𝗽 𝗨𝘀𝗶𝗻𝗴 𝗢𝗻𝗲 𝗠𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴
Running a 70B model to summarize a short email is wasteful. Using a 3B model to review code is risky. Most systems fall in the middle. This is where model routing helps.
Routing matches task difficulty to model capability. It saves money and reduces wait times. Most people use one model for everything. This works until costs or speed become problems.
Use these four strategies:
• Capability-based: Route by what the model can do. • Cost-aware: Route by your budget. • Latency-aware: Route by how fast you need a response. • Hybrid: Combine all three.
Match your tasks to the right size:
- Classification and tagging: 1-3B models (e.g., Qwen2.5-1.5B).
- Summarization and extraction: 3-7B models (e.g., Llama-3.1-8B).
- Code generation: 7-14B models (e.g., DeepSeek-Coder).
- Complex reasoning: 14-32B models (e.g., Llama-3.1-70B).
- Creative writing and analysis: 32B+ models (e.g., GPT-4).
If a small model handles a task, do not use a large one. A 1.5B model handles sentiment analysis well. It just cannot write an essay.
Local models are a smart choice. They cost almost nothing after you buy the hardware. Running a local model can be much cheaper than paying for API tokens if you process thousands of requests.
Consider these use cases for speed:
- Real-time chat: Use models under 7B for instant responses.
- Interactive tools: Use models under 14B.
- Batch processing: Use any model size.
If you build a router, include a fallback chain. Start with the best model. If it fails or hits a limit, move to the next best one. The last model in your chain should be a local model. Local models do not fail due to network issues or API limits.
Routing adds complexity. Do not use it if every task you perform is the same difficulty. Start with one model. Add a router only when cost or speed becomes a problem.
Source: https://dev.to/rosgluk/model-routing-stop-using-one-model-for-everything-4mf1
Optional learning community: https://t.me/GyaanSetuAi