LLM-D Deep Dive: How Disaggregated LLM Inference Actually Works

A Reddit user asks why modern LLM inference stacks are adopting prefill-decode disaggregation, splitting the prefill and decode phases across different hardware. The question reflects growing industry interest in separating these phases to improve efficiency and throughput.

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Engineer shares lessons scaling LLM inference from one GPU to millions of users in new blog

An engineer who worked with inference providers scaling to millions of users published a Medium blog on production inference system design. It covers multi-node weight distribution, caching, autoscaling, and routing — challenges that go beyond single-node local optimization. The author notes these skills remain in demand and not fully automatable by agents.

An engineer who worked with inference providers scaling to millions of users published a Medium blog on production inference system design. It covers multi-node weight distribution, caching, autoscaling, and routing — challenges that go beyond single-node local optimization. The author notes these skills remain in demand and not fully automatable by agents.

A Medium column examines whether separating prefill and decode phases in LLM inference improves performance, concluding that the optimization freedom comes with costs. The piece argues that disaggregation only pays off under specific conditions, offering a nuanced view for practitioners.

A new open-source project called inference-cache provides a Kubernetes-native control plane for cache-aware LLM routing, aiming to reduce redundant computation of shared prefixes. It addresses a common inefficiency in serving repeated LLM prompts by leveraging cached prefixes.

A software engineer published the fourth and final part of a blog series explaining LLMs from the ground up, focusing on token-by-token generation, KV cache, and decoding strategies (temperature, top-k, top-p). The series aims to help other software engineers understand the internals of LLMs.