LLM Serving Fairness: How Cohere Eliminates the Noisy Neighbour Problem
Cohere Blog40 days agoCommentaryCohere's engineering blog addresses the "noisy neighbour" problem in multi-tenant LLM serving, where one tenant's heavy workload degrades performance for others sharing the same infrastructure. The post outlines how Cohere designs its serving layer to guarantee each tenant receives a fair and consistent share of compute resources. This is a practical look at production-grade fairness mechanisms relevant to any organisation relying on shared AI API infrastructure.
Real-Time LLM Inference on Standard GPUs at 3k Tokens/s per Request
Hacker News (AI keywords)60 days agoBenchmarkThe post’s title indicates a performance claim for real-time LLM inference on standard GPUs, reporting 3,000 tokens per second per request. No article body is available, so the underlying model, GPU type, batch size, latency profile, precision, serving stack, and benchmark method are not stated. The item is best treated as an inference-performance benchmark claim rather than a verified deployment guide.