Together AI shares principles for autoscaling LLM inference
Together AI has introduced a method for autoscaling dedicated endpoints for LLM inference to address cold starts and request queuing.
What happened?
Infrastructure company Together AI published a guide in November 2023 on how to effectively autoscale dedicated endpoints for LLM inference. The company highlights that traditional GPU utilisation is a misleading metric for inference, as GPU usage may appear normal even while request queues grow. Instead, it recommends using metrics based on queue time and latency to trigger scaling.
Key facts
| Publiceringsdatum | November 2023 |
|---|---|
| Tekniskt område | Autoskalning för LLM-inferens |
| Huvudutmaning | Uppvärmningstid för nya GPU-repliker |
”GPU utilization can read healthy while your queue backs up, and a new replica takes minutes to warm.”
Why it matters
Scaling GPU infrastructure for language models is technically challenging because new replicas can take several minutes to start and warm up, known as cold starts. Properly configured scaling windows and metrics prevent both user bottlenecks and unnecessary costs from idle computational resources.
Who is affected?
The news is primarily relevant to AI developers, system architects, and companies running their own dedicated language models in production. It is particularly pertinent for organisations managing varying traffic volumes that wish to optimise their GPU costs.
Impact on the EU
The scaling mechanism is available globally via Together AI's cloud infrastructure, including for European developers using the platform. There are no specific regulatory hurdles or EU-unique restrictions affecting this technology.
What else you should know
According to Together AI, effective scaling requires balancing response times against infrastructure costs. By combining the right metrics with optimised warm-up routines for new instances, companies can avoid unnecessary costs for idle GPU resources.
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