The Register
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2 months ago
● 3 sources
AI agents performing inference workloads generate unpredictable, concurrent data access patterns that overwhelm cloud storage systems designed for human-speed applications, requiring architectures that decouple performance from capacity. AWS EBS volumes experience burst credit exhaustion and latency spikes from 1 millisecond to 50+ milliseconds when AI inference traffic overwhelms the storage layer, as demonstrated by a fintech e-commerce platform whose AI shopping assistant caused a system-wide outage within 15 minutes of launch. Organizations must architect for extreme tail latency performance (p99/p999 under mixed load) and adopt software-defined storage solutions that can deliver consistent sub-millisecond latency even during concurrent OLTP and inference workloads, rather than attempting to scale through read replicas or additional IOPS provisioning.
Together AI
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2 months ago
● 3 sources
Together AI describes how inference costs dominate production AI systems at 80-90% of total lifetime cost, requiring optimization across latency, throughput, concurrency, and scheduling dimensions. The company has developed a full-stack approach including techniques like adaptive speculative decoding (Aurora delivering up to 1.25x speedup), custom hardware optimization on NVIDIA Blackwell systems, and intelligent batching that ship research to production within weeks. Better inference efficiency directly improves margins for AI-native companies by enabling more requests per GPU-hour and expanding economically viable use cases.
OpenAI Blog
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2 months ago
OpenAI rebuilt its WebRTC stack to handle real-time voice conversations with artificial intelligence at global scale. The system prioritizes low latency and seamless turn-taking, enabling responsive back-and-forth dialogue without noticeable delays. This infrastructure allows OpenAI to deploy voice AI features to users worldwide while maintaining the conversational quality needed for natural interaction.
