Cold starts & autoscaling
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Articles
31 August 2026
Autoscaling GPU Inference: KServe vs Ray Serve vs llm-d
KServe, Ray Serve, and llm-d offer different approaches to scaling GPU inference on Kubernetes. While KServe standardizes general model serving and Ray Serve enables Python-native pipelines, llm-d adds LLM-specific optimizations like disaggregated prefill and decode.
10 June 2026
Serverless GPU Cold Start Latency: Architecture Comparison
Scale-to-zero GPU infrastructure promises massive cost savings, but a 40-second cold start will kill any real-time AI application. Here is a technical breakdown of where the time actually goes and how modern inference stacks are solving the VRAM bottleneck.
23 April 2026
Serverless Inference Cold Start Latency: A Technical Optimization Guide
Cold starts remain the primary barrier to responsive serverless AI. This guide breaks down the technical stages of GPU initialization and provides a framework for minimizing latency in production environments.
21 April 2026
The Economics of Scale to Zero: Slashing GPU Inference Costs in 2026
Running dedicated GPU instances for bursty inference workloads is the fastest way to burn through venture capital. Scale-to-zero orchestration allows teams to eliminate idle compute costs without sacrificing the performance required for production-grade AI.
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