Multi-Modal Frontiers & Vector Architecture
Local SLMs in Production
Small language models run on modest hardware with low latency and predictable cost, making them attractive for edge and high-volume workloads. Success depends on picking the right model, quantizing carefully, and routing hard cases to larger models. Local does not mean simple; operations are yours.
Points clés
- Choose the smallest model that meets your accuracy target on your eval set.
- Quantize to 4 or 8 bit and benchmark accuracy loss on your own tasks.
- Serve with llama.cpp, vLLM, or Ollama based on throughput and hardware.
- Add a router that escalates hard or low-confidence cases to a larger model.
- Bound context length to protect memory and latency.
- Monitor throughput, memory, and error rates like any production service.
- Pin model versions and test updates before rolling them out.
- Secure the endpoint; a local model is still an API that can be abused.
Questions fréquentes
Are SLMs good enough for production?
For scoped tasks with strong evals, yes; for open-ended reasoning, often no.
Is local cheaper than an API?
At high volume or with sunk hardware, yes; at low volume, APIs usually win.
How do I handle model updates?
Pin versions, test on staging, and keep a rollback copy of weights.
What is the biggest SLM risk?
Overconfidence on cases outside the training distribution.
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